Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

 Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accidents and guaranteeing sufficient maintenance. This paper presents a method based on image processing for fault diagnosis of rotating machinery. Different from traditional methods of signal analysis in the one-dimensional space, this study employs computing methods in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, histogram of oriented gradient (HOG), is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form the feature vector. In the case study, two typical rotating machineries, gearbox and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.

This study presents a new manifold learning framework for machinery fault diagnosis, in order to further improve fault diagnosis accuracy. The new manifold learning framework contains two stages: unsupervised manifold learning for nonlinear denoising and supervised manifold learning for feature extraction. Firstly, the nonlinear denoising method with unsupervised manifold learning was introduced, which combined advantages of manifold learning in revealing nonlinear manifold structure as well as advantages of phase space reconstruction in representing spatial distribution of signal and noise. Then, fault feature extraction was carried out according to the frequency spectrum of vibration signals after denoising. In order to reduce the high dimension and remove redundant information of frequency spectrum, an improved supervised local tangent space alignment (ISLTSA) was proposed to further enlarge diversity of the fault samples and thus increase separability. Finally, the extracted low-dimensional fault features were inputted into a pattern recognition method for fault identification. The effectiveness of the proposed method was verified by studying the fault diagnosis of bearings.

Rolling element bearings are widely used in machinery, such as cooling fan, railway axle, centrifugal pump, transaction motor, gas turbine engine, wind turbine gearbox, etc., to support rotating shafts. Bearing failures will accelerate failures of other adjacent components and finally result in the breakdown of systems. To prevent any unexpected accidents and reduce economic loss, condition monitoring and fault diagnosis of rolling element bearings should be immediately conducted. Ensemble empirical mode decomposition (EEMD) as an improvement on empirical mode decomposition is a data-driven algorithm to adaptively decompose vibration signals collected from the casing of machinery for bearing fault feature extraction without the requirement of expertise and thus its easy usage attracts much attention in recent years from readers and engineers. The direct applications of EEMD to preprocessing bearing fault signals for intelligent bearing fault diagnosis can be found in lots of publications and conferences every year. However, such applications are not always effective in extracting bearing fault features because the Fourier spectrum of the first intrinsic mode function is too wide and contains many unwanted strong low-frequency periodic components. In this paper, according to results from the analyses of industrial railway axle bearing fault signals, we experimentally show that the direct use of EEMD is not always effective in extracting bearing fault features. Further, to make EEMD more effective, we introduce the concept of blind fault component separation to separate low-frequency periodic vibration components from high-frequency random repetitive transients, such as bearing fault signals. Results show that the idea of blind fault component separation is much helpful in enhancing the effectiveness of EEMD in extracting bearing fault features in the case of industrial railway axle bearing fault diagnosis.

Based on the deficiency in the traditional fault diagnosis method of rotating machinery, i.e. shallow learning is usually used to characterize complex mapping relationship between vibration signals and the rotor system, a deep neural network (DNN) based on stacked denoising autoencoder (SDAE) is proposed. The proposed method has been successfully applied to the fault diagnosis of rotating machinery. In the proposed method, the frequency domain information of vibration signal is used as input signal, and the deep neural network is obtained by layer-by-layer feature extraction from denoising autoencoder (DAE). Then the dropout method is used to adjust the network parameters, and reduces the over-fitting phenomenon. In additional, the principal component analysis is used to extract fault features. The experiment result shows that the proposed method is very effective, and can effectively extract the hidden features in the vibration signal of rotating machinery.

 The number of features for fault diagnosis in rotating machinery can be large due to the different available signals containing useful information. From an extensive set of available features, some of them are more adequate than other ones, to classify properly certain fault modes. The classic approach for feature selection aims at ranking the set of original features; nevertheless, in feature selection, it has been recognized that a set of best individually features does not necessarily lead to good classification. This paper proposes a framework for feature engineering to identify the set of features which can yield proper clusters of data. First, the framework uses ANOVA combined with Tukey’s test for ranking the significant features individually; next, a further analysis based on inter-cluster and intra-cluster distances is accomplished to rank subsets of significant features previously identified. Our contribution aims at discovering the subset of features that discriminates better the clusters of data associated to several faulty conditions of the mechanical devices, to build more robust multi-fault classifiers. Fault severity classification in rolling bearings is studied to verify the proposed framework, with data collected from a test bed under real conditions of speed and load on the rotating device.

 Gearboxes and bearings play an important role in industries for motion and torque transmission machines. Therefore, early diagnoses are sought to avoid unplanned shutdowns, catastrophic damage to the machine or human losses; additionally, an appropriate diagnosis contributes to increase productivity and reduce maintenance costs. This paper addresses a methodological framework for the diagnosis of multi-faults in rotating machinery through the use of features rankings. The classification uses K nearest neighbors and random forest, based on the information that comes from the measured vibration signal. Thirty features in time domain are calculated from the vibration signal, twenty-four features commonly used in fault diagnosis in rotating machinery, and six features are used from the field of electromyography. Feature ranking methods such as ReliefF algorithm, Chi-Square, and Information Gain are used to select the ten most relevant features, the same ones that enter the classifiers. Five databases were used to validate the proposed methodological framework. The results show good accuracy in classification for the five databases; furthermore, in all the databases in the first ten features ranked by the three rankings methods are present at least two nonconventional features.

Particulate matter (PM) is one of the most significant air pollutants in recent decades that has tremendous negative effects on the ambient air quality and the public health. Accurate PM forecasting provides a possibility for establishing an early warning system. In this paper, a deep feature learning architecture, i.e., autoencoder-based deep belief regression network (AE-based DBRN), is introduced and utilized to forecast the daily PM concentrations (PM_2.5 and PM₁₀). Prior to establishing this model, Pearson correlation analysis is applied to look for the possible input-output mapping, where the input candidate variables contain seven meteorological parameters and PM concentrations within one-day ahead, and the output variables are the local PM forecasts. The addressed model was evaluated by the dataset in the period of 28/10/2013 to 31/8/2016 in Chongqing municipality of China. Moreover, two shallow models, feed forward neural network and least squares support vector regression, were employed for the comparison. The results indicate that the AE-based DBRN model has remarkable better performances among the comparison models in terms of mean absolute percentage error (PM_2.5 21.092%, PM₁₀ 19.474%), root mean square error (PM_2.5 8.600μg/m³, PM₁₀ 11.239μg/m³) and correlation coefficient criteria (PM_2.5 0.840, PM₁₀ 0.826).

Local defects of rotating machinery give rise to periodic impulses in vibration. To acquire this fault information, many diagnostic methods have been reported in the past decades. Among them, the envelope spectrum analysis is usually used as the final diagnostic tool; however, its success highly depends on the correct informative frequency band selection. The key problem is how to find the correct centre frequency and its related bandwidth associated to the fault. In this paper, a novel method is proposed for selection of the optimal frequency band parameters. This method improves the informative frequency band selection performance with two aspects. One is that it incorporates the normal data as a health reference, and the other is that an objective indicator that could fuse multidimensional information is proposed. An optimal frequency band can be obtained through this algorithm, and fault mode is then determined via comparing the squared envelope spectrum between the test and normal signals. At the end of this paper, the proposed method is validated on two diagnosis cases and is compared with two of the other diagnostic methods: the conventional envelope analysis and the kurtogram. Though comparison of the results, the validity and superiority of the proposed method have been proven.

To overcome the low diagnosis accuracy caused by the scarcity of labeled training samples, a fault diagnosis method was proposed using orthogonal Semi-supervised linear local tangent space alignment (OSSLLTSA) for feature extraction and transductive support vector machine (TSVM) for fault identification. Through extracting the statistical features were extracted from the sub-bands of vibration signals decomposed by wavelet packet decomposition (WPD), the high-dimensional feature set could be obtained. Following that, the improved kernel space distance evaluation method was applied to remove non-sensitive fault features. Then, a semi-supervised manifold learning method (OSSLLTSA) was proposed to reduce the dimensionality of the fault feature set, and thus to extract fused fault features with high clustering performance. OSSLLTSA overcomes the over-learning of supervised manifold learning and projection aimlessness of unsupervised manifold learning. Finally, the low-dimensional feature set after dimension reduction was inputted into TSVM for fault diagnosis. TSVM was able to completely utilize the fault information contained in unlabelled samples to modify the model, and the trained fault diagnosis model has better generalization ability. The effectiveness of the proposed method was verified based on the case of gearbox fault. Experimental results showed that the proposed method is able to achieve very high fault diagnosis accuracy even when labeled samples were insufficient.

It is a great challenge to accurately and automatically identify different faults of the key components in rotating machinery. In this paper, a new method called feature fusion deep belief network is proposed for the intelligent fault diagnosis of rolling bearing. Firstly, a deep belief network (DBN) is constructed with several pre-trained restricted Boltzmann machines for feature learning of the raw vibration data. Secondly, locality preserving projection (LPP) is adopted to fuse the deep features to further enhance the quality of the learned deep features. Finally, the fusion deep features are fed into Softmax for automatic and accurate fault diagnosis. The proposed method is applied to analyze the experimental rolling bearing signals, and the results show that the proposed method is more effective than the traditional intelligent diagnosis methods.

Reliable degradation prognosis of mechanical components is very important for condition-based maintenance to improve the reliability and reduce the cost of maintenance. This paper reports the development of a fuzzy feature fusion and multimodal regression method for the degradation prognosis of mechanical components. Initially, the raw features from the vibration signals of the mechanical components are extracted. A degradation index is subsequently yielded by merging the obtained features through/using the fuzzy fusion technique. The ensemble empirical mode decomposition is then introduced to decompose the fusion index into several multimodal sub-series to acquire more detailed information. Extreme learning machines are established to predict the sub-series in different modes. The predicted results are obtained by integrating the multimodal sub-results. The reported approach was evaluated with real data from a rolling element bearing. Moreover, two peer models were imported to validate the effectiveness of the proposed method. The experimental results indicate that the reported approach is capable of erecting the degradation index reflecting the bearing degradation and that it had better performance in the remaining useful life prediction than the peer methods.

On-line anomaly detection is critical for the safety of unmanned aerial vehicles (UAVs). However, the flight status assessment still depends on ground control stations, which cannot meet the time requirement for autonomous and safe flight. The lack of on-board intelligent anomaly detection systems makes it rather difficult for on-line flight status estimation and assessment. In order to achieve real-time monitoring of UAV flight status and enhance the reliability and safety of UAVs, an embedded intelligent system is designed to address the challenging issues of UAV on-line anomaly detection in this paper. During the flight, the status of sensors and key components are continuously detected via flight data which can reflect the current status of the UAV. The proposed embedded anomaly detection system includes two main parts: (1) a general heterogeneous computing architecture which is based on Xilinx Zynq-7000 SoC with dual-core Cortex A9 processors and Field Programmable Gate Arrays (FPGA), (2) an on-line anomaly detection intelligent algorithm which is based on least squares support vector machine (LS-SVM) prediction model and utilized as a demonstration that needs high computing performance. The simulation flight data are used to verify the proposed system, and the experimental results show that the proposed intelligent system is capable of effective UAV on-line anomaly detection.

Bearing fault diagnosis under variable speed often faces two obstacles: a) blurry time frequency representation (TFR) and thus ambiguous and even unattainable instantaneous frequency (IF) for resampling, and b) complicated and error-prone resampling processes. To address such problems, this paper proposes a new tacholess and resampling-free method for bearing fault diagnosis under variable speed conditions. This method consists of two main steps: a) extract an accurate IF from the vibration data following a dual pre-IF integration strategy and a regional peak search algorithm to search the frequency bins point by point at local frequency regions, and b) with the accurate IF estimator (either shaft IF, instantaneous fault characteristic frequency (IFCF) or their harmonics), multi-demodulate the signal and superpose the resulting frequency spectra of all demodulated signal components using an order peak highlighting method. Then, the instantaneous frequency order (IFO) of signal components of interest contained in the original signal can be highlighted and the IFO spectra can be obtained for bearing fault diagnosis under variable speed conditions. In this manner, the bearing fault can be diagnosed without tachometer devices and resampling procedure. Therefore, the proposed method can substantially reduce human involvement and facilitate its implementation in a fault detection expert system. The effectiveness of the proposed method is validated using both simulated and experimental data.

Bearings are one of the most omnipresent and vulnerable components in rotary machinery such as motors, generators, gearboxes, or wind turbines. The consequences of a bearing fault range from production losses to critical safety issues. To mitigate these consequences condition based maintenance is gaining momentum. This is based on a variety of fault diagnosis techniques where fuzzy clustering plays an important role as it can be used in fault detection, classification, and prognosis. A variety of clustering algorithms have been proposed and applied in this context. However, when the extensive literature on this topic is investigated, it is not clear which clustering algorithm is the most suitable, if any. In an attempt to bridge this gap, in this study four representative fuzzy clustering algorithms are compared under the same experimental realistic conditions: fuzzy c-means (FCM), the Gustafson-Kessel algorithm, FN-DBSCAN, and FCMFP. The study considers only real-world bearing vibration data coming from both a benchmark data set (CWRU) and from a lab setup where interference between bearing faults can be studied. The comparison takes into account the quality of the generated partitions measured by the external quality (Rand and Adjusted Rand) indexes. The conclusions of the study are grounded in statistical tests of hypotheses.

Fault diagnosis plays a crucial role to maintain healthy conditions in rotating machinery. In real industrial applications, a Machine Learning based Classifier (ML-C) analyses data from a current machinery condition to detect abnormal behaviours. Usually, this is achieved through a previous training of the ML-C model, under supervised learning; however, for new machinery conditions, the classifier is not able to correctly identify these new condition. This paper proposes a framework to detect new patterns of abnormal conditions in gearboxes, that could be associated to new faults. The framework relies on an algorithm to build evolving models in simultaneous scenarios of classification and clustering. The design is inspired by the main principles of the K-means and the One Nearest Neighbour (1-NN) algorithms. A heuristic metric is defined to analyse the new discovered clusters; as a result, these new clusters can be labelled as new classes corresponding to new faulty patterns. Once a new pattern is identified, the associated data feeds a dedicated supervised classifier which is updated through a new training phase. The proposed framework is tested on data collected from a gearbox test bed under realistic conditions of faults. Experimental results show that the algorithm is able to discover new valuable knowledge than can be identified as new faulty classes.

Machine learning is widely used for fault diagnosis research. In general, most models used for fault diagnosis are based on the same data distribution, whereas applying equipment to practical productions and operations are mostly done under variable conditions. This often produces changes in data distribution and makes the model unavailable. As one of the most commonly used pieces of equipment in industry, a reciprocating compressor operates under various operating conditions (e.g., variable speed), which may produce changes in data distribution. Thus, the current model established under stable conditions is no longer applicable for fault diagnosis under variable conditions. To solve this problem of variable conditions, a model should be established that 1) reduces the differences caused by different operating conditions as much as possible, and 2) learns representative fault features under different working conditions. Thus, a new strategy that employs an auxiliary model is proposed that combines a convolutional neural network (CNN) and a marginalized stacked denoising autoencoder (mSDA). In our method, 1) the pre-training model CNN is used for feature learning, and 2) the learned features are transformed by mSDA to eliminate data distribution differences between different conditions. A statistical measure based on kernel maximum mean discrepancy is used to evaluate the differences across different domains. Experimental results of a reciprocating compressor under different operating conditions demonstrate that the proposed method can learn class sensitive features and eliminate differences with changing working conditions. It also obtains higher classification accuracy for reciprocating compressor diagnosis under different working conditions.

Fault detection in rotating machinery is important for optimizing maintenance chores and avoiding severe damages to other parts. Signal processing based fault detection is usually performed by considering classical techniques for alternative representation of significant signals in time domain, frequency domain or time-frequency domain.

An approach based on dictionary learning for sparse representations of vibration signals aiming at gearbox fault detection and classification is proposed. A gearbox signal dataset with 900 records considering the normal case and nine fault classes is analyzed. A dictionary is learned by using a training set of signals from the normal case. This dictionary is used for obtaining the sparse representation of signals in the test set and the norm metric is used to measure the residual from the sparse representation. The extracted features are useful for machine learning based fault detection. The analysis is performed considering different load conditions. ANOVA statistical analysis shows that there are significant differences between features in the normal case and each of the faulty classes, and best ranked features form well separated clusters. An experiment of fault classification is developed using a support vector machine for multi-class classification of faults. The accuracy obtained is 95.1% in the cross-validation testing.

A planetary gearbox is a crucial but failure-prone component in rotating machinery, therefore an intelligent and integrated approach based on impulsive signals, deep belief networks (DBNs) and feature uniformation is proposed in this paper to achieve real-time and accurate fault diagnosis. Since the gear faults usually generate the repetitive impulses, an integrated approach using the optimized Morlet wavelet transform, kurtosis index and soft-thresholding is applied to extract impulse components from original signals. Then time-domain features and frequency-domain features are calculated by both original signals and impulsive signals, and probability density functions are applied to study the sensitivities of the features to the faults. The extracted features are fed into DBNs to identify the fault types, and the results show that the DBN-based fault diagnosis method is feasible and the impulsive signals play a positive role to improve the accuracies. Finally, by the mean value of various signals under multiple load conditions, uniformed time-domain features are constructed to reduce the interference of loads, and the experimental results validate that feature uniformation can improve the accuracies and robustness of intelligent fault diagnosis approach.

In order to solve the problems that traditional diagnostic method is heavily dependent on the signal processing techniques and expert experience, and the diagnostic accuracy is difficult to have big improvement anymore with the accumulation of operational data, which cannot meet the needs of fault diagnosis in the big data age, a multi-source signals feature fusion method by deep learning model is proposed in this paper. The stacked denoising autoencoders (SDAE) is used to extract the abstract and deep features from original features, and then locality preserving projections (LPP) is used for dimensionality reduction to complete the feature fusion. Finally, the fused low-dimensional features act as inputs to the support vector machine (SVM) to realize the failure detection and fault location of typical fault modes of the landing gear hydraulic retraction system. The inhibitory effect of the feedback control on the incipient fault is discussed as well. Moreover, a severity assessment method is presented considering the gradual degradation of leakage fault of the actuator. The diagnostic results show that the proposed method has a better feature fusion ability and higher diagnostic accuracy. The health assessment model can evaluate the health state of the actuator. The significance of this paper is to provide a feasible idea for the fault diagnosis of the landing gear hydraulic retraction system and health assessment of the actuator.

Vibration signals generated from gears often exhibit nonlinearity. Characterization of such signals using nonlinear time series analysis can be a good alternative for identifying gear faults. This paper presets a recurrence network based approach to extract features from vibration signals for gear fault diagnosis. Quantitative parameters (such as mean degree centrality, global clustering coefficient, assortativity of the recurrence network, or network entropy) related to the dynamical complexity of the vibration signals are calculated from the generated recurrence network to help classify different gear faults with two kinds of classifiers, i.e., support vector machine and extreme learning machine. Experimental studies performed on two different gear test systems have verified the effectiveness of the presented recurrence network approach for gear fault severity evaluation, as well as gear fault classification.

Condition monitoring is an effective methodology to evaluate the health state of power electronics converters. Aiming at multiple devices health state estimation for boost converters, a non-invasive condition monitoring technique is proposed in this paper. Taking the equivalent circuit model of these components into consideration, the formulations of failure precursors with detection signals are derived based on hybrid system theory. Then, the parameter identification problem is translated into an objective function optimization issue. Therefore, the precursor parameter values of inductor, capacitor, diode and power MOSFET can be obtained using crow search algorithm. Meanwhile, the boost converters under variable operating conditions are also analyzed. Compared with particle swarm optimization (PSO) method, both simulations and experiments are conducted to validate the effectiveness of the presented approach. The results show that these parameters can be estimated simultaneously and the identification accuracy of them reaches to more than 90%.

Machine learning based intelligent diagnosis methods can adaptively generate the fault diagnosis model by historical data, which have attracted much attention. Artificial neural network (ANN) is one of the most important tools for gearbox intelligent diagnosis. However, the training of ANN has the problem of local optima, and it is hard to determine the ANN structure. These problems have great influence on the diagnosis performance of ANN. In this paper, a variable neural network (RegPSOVNN) is proposed for gearbox fault diagnosis based on regrouping particle swarm optimization. Ten time-domain features are selected to form the input of the ANN. Regrouping particle swarm optimization (RegPSO) is utilized for the optimization of ANN structure and network training. It can simultaneously optimize the structure and parameters of ANN and effectively avoid the problem of local optima. To evaluate the diagnosis performance of the proposed method, gearbox failure experiments were conducted, and backpropagation neural network (BPNN), firefly variable neural network (FAVNN) and particle swarm optimization based variable neural network (PSOVNN) were used for comparison. Experimental results indicated that the proposed method can effectively optimize the network structure and diagnosis the gearbox faults.

In allusion to performance degradation condition recognition issue for rolling bearing, a method based on improved pattern spectrum entropy (abbreviated as IPSE) and fuzzy C-means algorithm (abbreviated as FCM) is proposed in this paper. Basic pattern spectrum analysis is improved by introducing morphological corrosion operator and IPSE is proposed as the degradation feature parameter in describing bearing performance degradation degree. Simulation analysis shows that IPSE value will increase monotonously along with the deepening of the degradation degree. IPSE and degradation degree has a stable relevance. On this basis, in consideration of the fuzzy character of performance degradation condition boundary, FCM is introduced in degradation condition recognition so that the degradation condition could be recognized effectively in line with maximum subordination degree principle. Rolling bearing fatigue life enhancement testing was carried out in Hangzhou Bearing Test & Research Center, the whole life data was gathered and applied using the proposed technique. The classification coefficient reaches 0.9849 and average fuzzy entropy gets 0.0239 for training set clustering, meanwhile, the whole recognition ratio reaches 90% for testing set. The analysis shows that the technique has a good clustering effect and an acceptable recognition result.

As the essential component of prognostic and health management (PHM), life prediction for equipment plays a more and more significant role in recent years. However, current studies cannot fully consider the influence of imperfect maintenance activities that the equipment may experience on the degradation process and prognostic result. In this paper, we propose a degradation model subjected to the influence of imperfect maintenance for life prediction. Firstly, the multi-stage Wiener process is employed to characterize the influence of imperfect maintenance activities on the degradation level and degradation rate. Then, the theoretical expression of life probability distribution is derived under the concept of first hitting time using the convolution operation, and the approximate expression of life probability distribution is evaluated by the Monte Carlo simulation algorithm. Furthermore, we utilize the maximum likelihood estimation (MLE) to estimate unknown parameters in the concerned model. Finally, a numerical example and a practical case study are provided to substantiate the practicality and effectiveness of the newly proposed life prediction method. The results indicate that the proposed model can guarantee that the relative error (RE) is almost below 5%.

Accelerated degradation testing (ADT) has been widely used to accelerate failure/degradation processes and to quickly evaluate the reliability and lifetime of products. In particular, the application of copula function provides a convenient and efficient way to model the ADT data of products that have two or more s-dependent degradation measures. However, little effort has focused on the pointwise infimum and supremum of the multivariate joint-distribution function. For this paper, a novel prognostics method was developed for bivariate ADT data on the basis of Brownian motion and time-varying copula method, which can estimate the pointwise best-possible bounds on bivariate joint reliable life function with a given measure of association, such as Kendall’s τ or Spearman’s ρ. The proposed model is applied to the real ADT data of microwave assembly to illustrate its performance and effectiveness.

Roller bearings are among the most frequently encountered components in the majority of rotating machines. Thus, prognostic and health management of roller bearing plays an important role on the working conditions of the machine. Remaining useful life prediction is one of keys to apply PHM for practical applications. The collected bearing vibration signals are generally non-linear and non-stationary. However, those auto-regression model based methods are only suitable for the prediction of linear and stationary time series. Moreover, most of the existing machine learning based techniques require considerable training and parameter tunings which are time consuming and difficult for practical applications. To overcome these issues, a novel remaining useful life prediction method for rolling bearing prognostics is proposed in this work based on the sparse coding and sparse linear auto-regression model without training and parameter tunings. Sparse coding is formulated as a basis pursuit L₁-norm problem, where a sparse set of weight can be estimated for each test vector. Sparse local linear and neighbor embedding are employed to construct the proposed weight constraint sparse coding method. Two different experimental validations are conducted to well demonstrate the effectiveness and robustness of the proposed method for remaining useful life prediction of bearing via root-mean-square, peak-to-peak and kurtosis indicators in time-domain.

Predicting the remaining useful life (RUL) of rolling element bearings (REBs) has emerged as a vital technique for guaranteeing the safety, availability, and efficiency of rotating machinery systems. An approach using locally linear fusion regression (LLFR) is developed for the RUL prediction of REBs. The original features, derived from the time domain and time– frequency domain of the vibration signal of the REBs, are extracted first. Utilizing locally linear embedding, the extracted features are then fused into a condition indicator reflecting the entire degradation process. The adaptive network-based fuzzy inference system is then introduced for the RUL prediction. The reported approach is investigated with real REB data. Peer models are employed to validate the performance of the proposed method in this work. The derived experimental results indicate that LLFR has superior prediction ability as compared to the peer models in terms of the introduced performance criteria and that it can obtain more reliable and precise prediction results.

The prediction of performance degradation is significant for the health monitoring of rolling bearing, which helps to greatly reduce the loss caused by potential faults in the entire life cycle of rotating machinery. As a new method of machine learning based on statistical learning theory, a so-called multivariable least squares support vector machines (LS-SVM) was developed. However, it is unsatisfactory for the prediction of performance degradation without adequate consideration of time variation and data volatility, which are notable features of the obtained time series signal from bearings. To overcome these problems, a new multivariable LS-SVM with a moving window over time slices is proposed. In this model, different features over time slices are extracted through a moving window to construct new sample pairs according to the embedding theory. The model adaptability is also improved through an iterative updating strategy. Furthermore, the algorithm parameters are optimized using coupled simulated annealing to improve the prediction accuracy. Bearing fault experiments show that the proposed model outperforms the general multivariable LS-SVM.

Lithium Ion batteries usually degrade to an unacceptable capacity level after hundreds or even thousands of charge and discharge cycles. The continuously observed capacity fade data over time and their internal structure can be informative for constructing capacity fade models. This paper applies a mean-covariance decomposition (MCD) modeling method using data within moving windows to analyze the capacity fade process. The proposed approach directly examines the variances and correlations in data of interest and reparameterize the correlation matrix in hyper-spherical coordinates using angle and trigonometric functions. To improve the interpretation of the prognostics model, the mean function is obtained based on physics of failure. Non-parametric methods are used to characterize the log variance and correlation through the number of cycles and time lags between capacity measurements, respectively. A numerical example is used to illustrate the superiority of the proposed method in prediction performance.

Reciprocating compressors are widely used in oil and gas industry for gas transport, lift and injection. Critical compressors that compress flammable gases and operate at high speeds are high priority equipment on maintenance improvement lists. Identifying the root causes of faults and estimating remaining usable time for reciprocating compressors could potentially reduce downtime and maintenance costs, and improve safety and availability. In this study, Canonical Variate Analysis (CVA), Cox Proportional Hazard (CPHM) and Support Vector Regression (SVR) models are employed to identify fault related variables and predict remaining usable time based on sensory data acquired from an operational industrial reciprocating compressor. 2-D contribution plots for CVA-based residual and state spaces were developed to identify variables that are closely related to compressor faults. Furthermore, a SVR model was used as a prognostic tool following training with failure rate vectors obtained from the CPHM and health indicators obtained from the CVA model. The trained SVR model was utilized to estimate the failure degradation rate and remaining useful life of the compressor. The results indicate that the proposed method can be effectively used in real industrial processes to perform fault diagnosis and prognosis.

Telemetry data, sent by the satellite, is the only basis for ground staffs to monitor on-board equipment status. In addition, the pattern discovery and operating state identification of telemetry data are very essential for automatic anomaly detection and problem diagnosis for satellites. Clustering, as an important data mining method for time series, can realize pattern discovery of satellite telemetry data automatically and intelligently, whereas the large amount of raw data and pseudo-period characteristic make the clustering on raw data inefficient and susceptible to noise interference. Thus, based on the prominent shape features and Time-Spatial specialty, a clustering framework is proposed for telemetry data mining with physical-based segmentation and improved time series representation. Moreover, different distance measures are introduced to this framework to realize the time series clustering. The experiments are firstly performed on the public data sets which have high similarity with the real satellite telemetry to quantify the clustering accuracy, then a case study on the real satellite telemetry verifies the effectiveness and applicability of the proposed framework.

Usually, time series acquired from some measurement in a dynamical system are the main source of information about its internal structure and complex behavior. In this situation, trying to predict a future state or to classify internal features in the system becomes a challenging task that requires adequate conceptual and computational tools as well as appropriate datasets. A specially difficult case can be found in the problems framed under one-class learning. In an attempt to sidestep this issue, we present a machine learning methodology based in Reservoir Computing and Variational Inference. In our setting, the dynamical system generating the time series is modeled by an Echo State Network (ESN), and the parameters of the ESN are defined by an expressive probability distribution which is represented as a Variational Autoencoder. As a proof of its applicability, we show some results obtained in the context of condition-based maintenance in rotating machinery, where vibration signals can be measured from the system, our goal is fault detection in helical gearboxes under realistic operating conditions. The results show that our model is able, after trained only with healthy conditions, to discriminate successfully between healthy and faulty conditions and overcome other classical methodologies.

Wind speed forecasting is a prerequisite and a guarantee for the efficient operation of wind farms. Estimation of the non-stationary property and prediction of the future moving average of wind speed are challenging because of various environmental factors. In this paper, a new model is proposed for wind speed forecasting. The proposed model consists of three main stages: preprocessing, regression, and aggregation. In the first stage, the original wind speed time series is decomposed into different subseries by using the variational mode decomposition technique. These subseries are then used to construct training patterns and forecasted outputs. In the second stage, support vector regression is applied to fit and forecast the wind speed for each subseries. Eventually, the final forecasted wind speed is calculated by summing all the forecasting values of each subseries. The performance of the proposed model is evaluated using real data collected from a wind farm in China, and the experimental results confirm the superiority of the proposed model to the existing models with respect to accurate forecasting and stability.

The integrated determination of the charge batching and casting start time (CBCST) is a combinatorial optimization problem extracted from the production and operations management of steel plants. A hierarchical optimization method based on variable neighborhood search (VNS) is proposed in this paper for integrated determination of CBCST. The number of casts on each continuous caster and the number of charges in each cast are determined in the encoding. The decoding process is decomposed into solving two sub-problems. A mixed integer programming (MIP) model is built for the first sub-problem by considering it as a prize collecting multiple traveling salesmen problem, and a VNS algorithm is proposed for solving the model. In addition, another MIP model is developed for the second sub-problem, and the model is solved by CPLEX directly. Experimental results on practical production data demonstrate that the proposed algorithm is effective for determining the charge batching and the casting start time simultaneously.

Air is regarded as one of a fundamental element for the survival of human and other living creatures. Daily PM₁₀ concentration forecasting is a useful measure that is applied to the prevention and control of work in advance. This paper proposes a multiscale fusion support vector regression (MFSVR) method for forecasting daily PM₁₀ concentration. The method uses stationary wavelet transform (SWT) to decompose original time series of daily PM₁₀ concentration into different scales, of which the information represents wavelet coefficients of PM₁₀ concentration. At each scale, wavelet coefficients are used for training a support vector regression (SVR) model. The estimated coefficients of the SVR outputs for all of the scales applied to the reconstruction of the prediction result by the inverse SWT. To enhance forecasting of the MFSVR, a feature fusion approach that bases on partial least squares is adopted to extract the original features and reduce dimensions for input variables of the SVR model. The experimental confirmation of the proposed method is tested by applying the data of four monitoring stations between 1/1/2015 and 26/12/2015 in Lanzhou, China. The results indicate that the MFSVR approach can precisely forecast daily PM₁₀ concentration on the basis of mean absolute error, mean absolute percentage error, root mean square error and correlation coefficient criteria. This method shows a potential prospect that can be implemented in air quality prediction systems in other areas.

American Water Works Association has estimated that, by 2050, the total cost of pipeline system management will exceed $1.7 trillion. Thus, it is important to assess the performance of water mains in order to optimize the rehabilitation process. Recently, the use of machine learning methods in pipeline condition prediction has increased. However, existing pipe performance prediction models rely solely on underlying data-generating distributions and do not accommodate different datasets. Hence, a stacking ensemble based method is proposed in this work to overcome the drawbacks of the existing models and improve the predictive power of this mode of analysis. Using soil property data, both a single-model and an ensemble-model were constructed to forecast the pipe condition, and their prediction performance was compared and contrasted. Finally, the superiority of the proposed ensemble method was verified through its lowest value in the root-mean-square error relative to the individual models. The techniques presented in this work can aid in a reliable decision making in infrastructure management of buried pipeline networks.

Bearings are essential parts in mechanical transmission systems, and their running states directly affect the reliability and stability of the systems. Therefore, an efficient diagnosis method is necessary to detect faults in bearings. In the present, a simulation model based fault diagnosis method for bears is proposed by combination of finite element method (FEM), wavelet packet transform (WPT) and support vector machine (SVM). In this method, firstly, the agreeable finite element models to simulate faulty bearings are presented to obtain the vibration response signals. Secondly, the vibration signals are decomposed into eight signal components using WPT. Ten time-domain feature parameters of all the signal components are calculated to generate the training samples to train the SVM. Finally, the eight signal components decomposed by WPT from the measured vibration signal in a bear, which are serve as a test sample into the trained SVM, and the work condition of the bearing can be determined. Experimental investigations are performed to verify the effectiveness of the present method. The classification accuracy rates for four type faults, i.e., inner race fault, rolling body fault, outer race fault and the combination of rolling body and outer race faults, are 79%, 81%, 71% and 76%, respectively.

Planetary gearbox (PG), especially multi-stage PG, is widely used in various commercial and military applications. A dynamic model of PG is a valuable way to investigate its vibration behaviors in good and faulty statuses for designing a highly reliable PG and corresponding robust monitoring techniques. In this paper, a modified dynamic model is firstly constructed for a two-stage PG with a varying crack. The modifications include the crack propagation path settings as well as the coupled structure of flexible and rigid gear components, which makes a balance between the model accuracy and computational burden. Secondly, using this model, its vibration responses from a healthy PG and faulty PGs with different cracks are collected for the analyses of its frequency components and statistic features. Experiments are also conducted on a two-stage PG in similar conditions. Both results in simulations and experiments demonstrate the usefulness of the presented dynamic model. Moreover, sidebands and some statistic features are verified to be useful tools to monitor and evaluate the healthy status of PG.

With the explosive growth of the Internet, the Attribute-Based Access Control (ABAC) System has developed rapidly in size and complexity. So it is important to identify potential security flaws and bugs in the ABAC System using efficient testing. However, exhaustive testing is unrealistic due to time and budget constraints. In this paper, we propose a new method of unit testing based on the coverage selection approach using a decision inheritance tree. This data flow based testing method can achieve high structural coverage of design test cases. To evaluate the effectiveness of this method, we conducted two sets of tests using mutation operators and found higher mutation scores using the coverage selection approach based on the decision inheritance tree and combination testing based on data flow. The evaluation results show that the new method can reach higher mutation scores.

This paper presents an interval type-2 Takagi Sugeno (TS) fuzzy model of Angle of Attack sensor of the aircraft. The angle of attack signal is used as a gain scheduling variable in flight control computer for governing the flight control laws, hence any failure or malfunction of this sensor can cause catastrophic damage of the aircraft. In this paper angle of attack signal is estimated indirectly by means of interval type-2 TS fuzzy model with the data obtained from aircraft speed, vertical acceleration and pitch angle sensors. The methodology shows its benefits for formulating a data driven standby model of actual angle of attack sensor. The presence of uncertainty in data or measurements can be addressed by type-2 fuzzy logic theory. The concept is demonstrated by using the recorded flight data of VFW-614 ATTAS aircraft operating in quasi-steady stall flight maneuver without any reference to physics of stall hysteresis. The model parameters are obtained using Gustafson and Kessel (G-K) clustering and weighted least square method. The comparative study with other modeling methods and fivefold cross validation test between model estimated data and actual angle of attack sensor data shows well suited modeling capability of proposed interval type-2 TS fuzzy system.

Distance and similarity measures have recently been investigated in-depth within the context of hesitant fuzzy sets. By analyzing the existing studies concerning distance measures for hesitant fuzzy sets, we find that they have some limitations. To address the flaws, this study develops some novel distance measures for hesitant fuzzy sets, including the normalized Euclidean distance measure, the Hausdorff metric distance measure, the normalized generalized distance measure, and their corresponding weighted distance measures. The proposals of this study not only hold many ideal characteristics but also do not consider the lengths of hesitant fuzzy elements as well as the arrangement of their possible values. To deal with the situations where both of the universe of discourse and the weight of element are continuous, some continuous hesitant fuzzy distance measures are also investigated. Based on the relationship between distance measure and similarity measure, some novel similarity measures for hesitant fuzzy sets can be further deduced from the proposed distance measures. Finally, two numerical examples are given to demonstrate the applicability and validity of the proposed hesitant fuzzy distance measures.

In order to deal with the large amount of uncertain information in the fault diagnosis of gas turbine, intuitionistic fuzzy fault Petri nets (IFFPNs) model are constructed by combining the Intuitionistic Fuzzy Sets (IFSs) with Fuzzy Petri Nets (FPNs). According to the actual fault diagnosis process of gas turbine, we shall give a formal intuitionistic fuzzy reasoning algorithm with parameters such as weights, threshold value and certainty degree which are represented by intuitionistic fuzzy number. Based on the algorithm, the process of fault diagnosis can be transformed into intuitionistic fuzzy reasoning process. Finally, the feasibility and validity of the proposed inference model is illustrated by the instance of gas turbine fault diagnosis.

In this paper, we initiate to investigate the existence and uniqueness of solutions to initial value problems for fuzzy fractional Schrödinger equations involving the Caputo’s H-derivative. Continuous dependence on initial values of the solution is also considered. Our results are based on a successive approximation method and the Banach contraction mapping principle. Two examples are presented for our new results.

For complex multi-attribute large-group decision-making problems in the interval-valued intuitionistic fuzzy environment, decision attributes are correlated and stratified, and the correlations among them are not always consistent. This paper proposes a decision-making method: a two-stage regularized generalized canonical correlation analysis (RGCCA) based on multi-block analysis method. The proposed two-stage RGCCA method can well represent the different characteristics between the positive and negative attribute blocks, which makes the decision making process closer to actual. Since RGCCA can only handle single-valued information, this research also presents a novel transformation method of interval-valued intuitionistic fuzzy numbers to single-valued numbers. For the two-stage RGCCA model, in the first stage, all attributes are divided into the positive and negative attribute blocks according to the signs of the weight coefficients of block components. In the second stage, we conduct RGCCA based on multi-block analysis method for the two types of blocks, respectively. Finally, in terms of the estimated values of block components in the two types of blocks and weights of the two types of blocks (obtained by the maximizing deviation method), the evaluation value of each alternative is calculated and the ranking result of alternatives is given. An example is illustrated to verify the feasibility and the validity of the proposed method.

In a multipath environment, signals generally reach the receiving antenna through two or more paths, causing the existing direction-of-arrival (DOA) estimation methods to be biased. Various particle swarm optimization (PSO) algorithms may provide premature convergence without determining the global optimum. However, the multipath effect is reduced by projecting the received signal onto an appropriate beam space. In this paper, a new memetic PSO (MPSO) scheme is proposed for estimating the DOA in a multipath environment. This scheme addresses the multipath effect by applying local search techniques to PSO, resulting in highly efficient optimization and a reduced multipath effect. The scheme is as follows: First, PSO and the minimum variance distortionless response (MVDR) method are used to estimate the DOA of signals. Second, the individual best position of the swarm and the beam space MVDR (BMVDR) method are used to construct an objective function. Furthermore, the first-order Taylor expansion of the objective function and local search techniques are used to enhance the search accuracy and reduce the search complexity. Two numerical examples are presented to illustrate the design procedure and demonstrate the high performance of the proposed method.

This paper proposes a framework for trajectory tracking of wheeled robots in indoor environment. Being robust against uncertainty and scalability to large environments are essential factors for this task. Here, it is supposed that the robot is only equipped with a vision system e.g. a Kinect camera. Generally, some challenges in this problem are: determining suitable control architecture, adjusting the parameters of this architecture according to the given purposes, extracting proper features from high-dimensional input images. In this paper, using deep learning methods the proper features are extracted. The controller is designed based on weighted sum of these features. A new method to combine supervised learning and reinforcement learning is introduced to adjust the proposed controller parameters. The mobile robot and the experimental environments are established on the WEBOTS and MATLAB co-simulation platform. The simulation experimental results indicate that the designed control is robust and effective for tracking trajectories in the indoor environment.

 Occlusion handling is a challenging problem in object tracking. Most existing methods fail to handle well in complex image sequences. This paper presents a scene adaptive tracking algorithm in occlusion. We decompose the tracking into target translation and scale prediction. A kernelized correlation filter with an adaptive update scheme is adopted to estimate target position. The adaptive online update scheme takes advantage of the confidence score sensitivity to occlusion and reduces the false updating in occlusion during the tracking sequence. The target scale can be estimated by the correlation filter with the ridge regression. Extensive experiments results on 29 challenging occlusion sequences show that the proposed tracking approach achieves the average overlap precision (OP) of 72.2%, which improves the performance by 7.6% compared to the DSST. On OTB-50 dataset, our tracking approach is also superior comparing to several state-of-the-art trackers.

Fuzzy implications play important roles in both theoretic and applied communities of fuzzy set theory and have been extensively investigated. Fuzzy coimplications, as the dual operators of fuzzy implications, have received, however, little attention in the literature and, to the best of our knowledge, (T, N)-coimplications and residual coimplications are the only well-studied classes. On the other hand, many studies show that not all properties of fuzzy implications can be dualized in the standard way to fuzzy coimplications while fuzzy coimplications play key roles in quantum B-algebras, BCK-algebras, Heyting-Brouwer logic, fuzzy expert systems, etc. The present paper chooses to stand in the line of research on fuzzy coimplications. The notion of (O, N)-coimplication, obtained by relaxing the t-norm T in a (T, N)-coimplication to be an overlap function O that is used to model the degree to which an object is simultaneously supported by two different classes in decision making and image processing, is introduced, and basic properties and characterizations of some subclasses of (O, N)-coimplications are obtained.

Some fuzzy set and fuzzy relational inequations and equations are considered in the framework of a meet-continuous codomain lattice. The existence of maximal solutions to some fuzzy set inequations is proven. An algorithm is given for calculating the closure of a fuzzy set (relation) under the composition with a given fuzzy relation. Another algorithm for finding the transitive closure is proven to work in this framework.

In this paper, we have introduced λ- statistical convergence and condition of being λ- statistical Cauchy of real number sequences in fuzzy normed linear spaces. At the same time, in fuzzy normed spaces, we have introduced the concept of (V, λ) summability and (C, 1) summability, and then, we have studied the relation between these concepts and λ- statistical convergence.

 In this paper, applying the theory of L-fuzzy sets, we introduce the concept of an L-fuzzy ideal (L-fuzzy k-ideal) of an m-ary semigroup. Some properties of them are investigated and some structural theorems for L-fuzzy ideals (L-fuzzy k-ideals) of m-ary semigroups are proved. In this direction the concept of image and preimage of an L-fuzzy set under m-ary semigroup homomorphism are discussed. Also, the notions of normal L-fuzzy ideal (L-fuzzy k-ideal) and maximal L-fuzzy ideal (L-fuzzy k-ideal) of an m-ary semigroup are introduced and some properties of them are studied. Further, we introduce the notion of L-fuzzy congruence on an m-ary semigroup and make a study of L-fuzzy quotient m-ary semigroup using an L-fuzzy congruence, an L-fuzzy quotient m-ary semigroup induced by L-fuzzy ideals. We also investigate some properties of homomorphisms between them.

In order to achieve optimal selection of manufacturing services in cloud manufacturing environment, this paper proposed a novel hybrid MCDM approach integrated Rough Analytic Network Process (rough ANP) and Rough Technique for Order of Preference by Similarity to Ideal Solution (rough TOPSIS). In the first step, the novel evaluation method based on rough-ANP is proposed to determine weight of each indicator. In the second step, the decision-making system based on rough-TOPSIS is developed to compare and rank alternatives. At the same time, in group decision-making process, the concepts of rough number and rough boundary are introduced to express more integrated information. The novel approach makes use of the strength of rough set theory in handling vagueness and uncertainty, the superiority of Analytic Network Process (ANP) in non-independent hierarchy evaluation and the advantage of TOPSIS in multiple-objective decision analysis. Finally, a case study is presented to demonstrate the practicability and validity of the novel approach.

Objective image quality assessment plays an important role in various computer vision and image processing applications. The most widely used image quality measures are the classical mean squared error (MSE), computed by averaging the squared intensity differences of distorted and reference image pixels, and its related quantity of the peak signal-to-noise ratio (PSNR). Unfortunately, these measures are not very well matched to perceived visual quality. In this paper, we propose a measure based on the intuitionistic fuzzy set theory, whose performance would be more closely related to the human perception of visual quality. The proposed measure provides a flexible mathematical framework for modelling of imprecise or/and imperfect information often present in digital images. Furthermore, we show how the neighborhood-based intuitionistic fuzzy similarity measures can be combined with intuitionistic fuzzy inclusion measures for improving the perceptive behavior of intuitionistic fuzzy similarity measures. The performance of the proposed measure is evaluated on a set of real images with different distortion types and the obtained results demonstrate its advantages over the classical measures.

This paper describes a set of solution-set-invariant coefficient matrices of the fuzzy relation equation with max-min composition A ⊙ x = b or ⋁ j ∈ { 1 , 2 , ⋯ , n } ( a ij ∧ x j ) = b i for all i ∈ { 1 , 2 , ⋯ , m } with 1 ≥ b 1 ≥ b 2 ≥ ⋯ ≥ b m > 0 . An algorithm is given for determining the set scripfontA = {B ∣ {x ∣ B ⊙ x = b} = {x ∣ A ⊙ x = b}} completely.

In this paper, we analyse the robust stability and control problem of singular biological economic systems with uncertain items based on sliding mode control. Firstly, we establish a biological economic systems with uncertain parameters for the invasion of alien species. We get the uncertain T-S fuzzy singular systems by using a set of fuzzy rules and analyse the robust stability of nonautonomous uncertain T-S fuzzy singular systems. Then, we design the fuzzy integral sliding surface and controller. We prove that the state trajectories can arrive integral sliding surface in finite time, and we also provide the sufficient conditions that the sliding motion is globally asymptotically stable. Lastly, we give practical example to show the effectiveness and correctness of our conclusions about Scavenger invasion to Zhanjiang Chikan reservoirs. By using the real data and Fourier series to approximate the uncertain parameters, we verify the effectiveness and correctness of the sliding mode control.

In this paper, we develop a novel fuzzy data envelopment analysis (DEA) model, using fuzzy Choquet integral as an aggregating tool to evaluate the efficiencies of the decision making units (DMUs). The proposed model can be used to evaluate the fuzzy efficiency of the DMU with interactive fuzzy variables (fuzzy inputs or fuzzy outputs), and meanwhile, a ranking method of the fuzzy efficiency is introduced. At the end of the paper, we will use numerical examples to illustrate the performance of the proposed model.

 In this article, a new strategy to detect green plants in a maize crop has been developed. This strategy is based on five main stages: segmentation, reduction of dimensionality based on PCA, Otsu thresholding, threshold combination and final thresholding. Images are taken in RGB color model and transformed to grayscale image using basic vegetation index and PCA method to reduce the dimensionality of data. After this step, a combination of Otsu thresholds by PCA is designed in order to generate a new threshold and binarize the previous grayscale image. The performance of the proposed strategy is validated with an image set and compared with other strategies, being the best of all the methods analyzed in a quantitative mode.

In this work, the collocation method to solve fuzzy Volterra integral equations is introduced. Then, solving these systems by collocation method with Bernoulli polynomials is presented. Also, we will discuss the convergence of this method. In addition, the superiority of this method compared to other methods is proved, by solving of some systems of fuzzy Volterra integral equations.

An m-polar fuzzy model is a generalization of the fuzzy model. This model is useful for multi-polar information, multi-agent, multi-attribute and multi-object network models. In this research study, we introduce the notion of m-polar fuzzy directed hypergraphs, and present some properties. We depict certain operations on m-polar fuzzy directed hypergraphs. We also consider an application of m-polar fuzzy directed hypergraphs in business strategy company.

An attribute reduction can make the structure of concept lattices more convenient in formal fuzzy contexts. Thereby, it is beneficial to discover the knowledge. Based on the notion of “one-side fuzzy concept”, we propose a method of attribute reduction combining with the directed graph theory. Employing the proposed approach, a judgment theorem is given for determining concepts and irreducible elements in formal fuzzy contexts. According to the significance of attributes, these attributes are classified three types: core attributes, relatively necessary attributes and unnecessary attributes, which are referred to the attribute characteristics. Applying with the directed graph, We propose the judgment theorems and the corresponding algorithms for computing the three types of attributes sets. On this basis, a corresponding method of attribute reduction is given in a fuzzy-crisp formal context. The feasibility and effectiveness of the algorithm has been proved via an example. The approach presents a mew method for knowledge reducible in formal fuzzy contexts.

Machine translation is one of the parts of language processing within linguistic computing for automatic translation from one language to another. The paper introduces one of the most critical areas of soft computing and natural language processing, i.e. machine translation technique that is based on deep model structure of rough sets with capability to transfer learning. A deep rough set learning is developed to support machine translation to recognize and translate tens of thousands of words/sentences automatically. To our knowledge, this is the first attempt aiming to use rough sets in machine translation rather than Arabic language translation. A deep information table is learned by assigning the morphemes-similar objects with similar learning complexities into same class and it can identify the inter-related learning tasks automatically. To account for the differences among source-languages domains, we proposed a partial transfer learning scheme in which only part of source information is transferred. The experiments have demonstrated that the proposed model can achieve competitive results and significantly outperformed other methods for translation on both accuracy rates and the efficiency for machine translation.

Cubic sets are generalized version of fuzzy sets, in which there are two representations, one is used for the degree of membership and other is used for the degree of non-membership. Membership function is handled in the form of intervals while non-membership is handled through ordinary fuzzy sets. Since the invention of fuzzy set many researchers applied this notion to different algebraic structures. Mostly, they focus on the associative structures. Here we concentrate on a useful non associative structure known as H _v -LA-semigroup. Using this idea, we characterize an H _v -LA-semigroup in terms of cubic ideals. We study the idea of cubic equivalence relations, cubic regular relations in H _v -LA-semigroups and provide some related results.

Complex fuzzy sets have been successfully applied to many domains. In some applications, complex fuzzy operators play an vital role, especially those results depend on the particular choice of the conjunction, disjunction and complement operators. This paper introduces an equivalence relation on complex fuzzy sets, called parallelity. Then we provide a criterion that operators should satisfy the property of parallelity preserving, for the choices of complex fuzzy operators. Finally, several parallelity preserving inference methods are discussed for complex fuzzy reasoning.

Decision makers can be irresolute evaluating an alternative according to a criterion. In such cases, it is best to consider the hesitancy rather than force the decision maker to make a clear evaluation. This paper focused on how to use a fuzzy axiomatic design technique considering hesitancy. Especially, it was discussed whether triangular hesitant fuzzy numbers defined in the literature were suitable for fuzzy axiomatic design technique. Also the paper contained a humanitarian relief project in terms of the post-natural disaster temporary housing location selection problem. When a natural disaster occurs, temporary housing should be established in order to provide the life safety of natural disaster victims and support them. The main objective of this problem was to determine the best location that would meet requirements of the disaster victims as soon as possible and smoothly. In the case study of this paper, the best temporary housing location was determined in the event of a natural disaster in a city which locates on the North Anatolian Fault Line. The fuzzy axiomatic design technique was used considering hesitancy to make a choice between candidate locations in the City.

Disability badly affects the performance of daily activities of a person thereby degrading the quality of his life. Physical rehabilitation can help restoration of the physical functions of such persons to a great extent. Numerous research are being performed for developing muscle models capable of estimating torque, joint angles, force, velocity etc. especially for upper and lower limb kinematics. This paper proposes three models for estimating elbow angle namely, Average Value model, a feed forward back-propagation neural network model and a Non-linear Auto-Regressive with eXogenous input (NARX) neural network model. All these models are studied with the sEMG signals acquired from the biceps brachii muscles of ten healthy subjects. The linear envelope of the signal is utilized for constructing two time domain features which serve as inputs to the models. The output of the models is the estimated elbow angles corresponding to the human intention identified from the sEMG signals. Regression coefficient values and Root mean square error values are considered for evaluating the accuracies of the models. The results obtained show that the models can be effectively used for implementation in control of human limb prosthetics and assistive devices.

In some decision situations, decision makers sometimes cause this difficult problem with a few different single-valued neutrosophic values assigned by truth, indeterminacy, and falsity degrees due to decision makers’ hesitancy. Then, a single-valued neutrosophic hesitant fuzzy set (SVN-HFS) can express the hesitant information. Under a single-valued neutrosophic hesitant fuzzy environment, this paper introduces the extension method based on least common multiple cardinality for single-valued neutrosophic hesitant fuzzy elements (SVN-HFEs) and proposes the distance and similarity measures of SVN-HFSs. Then, we develop a multiple attribute decision-making (MADM) method by using the proposed similarity measure of SVN-HFSs. Finally, an illustrative example of investment alternatives is given to demonstrate the application and feasibility of the developed approach. The main advantage of the developed method is that it is more objective and more universal than the existing ones.

Since the emergence of digital revolution, many governments have sought to use the internet for the benefits of communication and information easiness to present public services. Due to citizen’s consciousness of internet, the online public services have increased rapidly. Allowing citizens to entrance governmental services, information and participate in governmental decision making process, called the electronic government. In recent years, various e-government websites have increased. Beside the basic lines for e-government websites design process, the evaluation approach also very important for increasing sites quality. The success of these sites depends largely on their quality, security and accessibility. This research provides a multi-criteria group decision making (MCGDM) technique rely on neutrosophic VIKOR method, for evaluating e-government websites. To represent preferences of decision makers about criteria significance weights and performance assessments, triangular neutrosophic numbers are used for representing linguistic variables. In this research, two algorithms are developed based on a neutrosophic linguistic approach. Depending on two algorithms and the VIKOR method, a general framework is proposed. A case study is presented to validate the proposed framework, and a comparative study between two algorithms is illustrated with detail.

One-class classification is an important problem encountered in a lot of applications. The datasets extracted from the real-world problems are often represented as tensors. The classical support vector domain description (SVDD) for one-class classification problems cannot work directly since its inputs are vectors. This paper develops a linear tensor-based algorithm named as Linear Support Tensor Domain Description (LSTDD) to find a closed hypersphere with the minimal volume in the tensor space which can contain almost entirely of the target samples. LSTDD can keep data topology and make the parameters need to be estimated less, and it is more suitable for learning the high dimensional and small sample size problem. Firstly, we detail the LSTDD model with 2nd-order tensors, and then extend it to the higher order tensors. It has been shown by experiments on the real-world datasets that LSTDD is a promising method for handling one-class classification problems with both 2nd-order and higher order tensor inputs.

With the increasing of the complexity of a system, there is a variety of indeterminacy in the practical applications of graph theory. We focus on uncertain random graph, in which some edges exist with degrees in probability measure and others exist with degrees in uncertain measure. In this paper, the chance theory is applied to construct the cycle index of an uncertain random graph. Then a method to calculate the cycle index of an uncertain random graph is presented. We also discuss some properties of the cycle index.

Asian option is known as a derivation financial product. And uncertain finance takes uncertain situation into account, and there comes uncertain stock models based on uncertain theory. This paper follows the mean-reverting stock model which based in uncertain situations presented by Liu. The mean-reverting model under asian option is discussed in this paper. This paper deals with the problem of pricing an Asian currency option. Based on the principle of making fair deal, the pricing formula is verified. Furthermore, a simple discussions about the situation of single variable in the option pricing model are also drawn in this paper. Basic relations between parameters and result are also discussed.

In this paper, the concept of M-fuzzifying geodesic interval operators is introduced in M-fuzzifying metric spaces. It is a generalization of geodesic interval operators in metric spaces. And an M-fuzzifying geometric characterization of M-fuzzifying geodesic interval operators is obtained. Based on M-fuzzifying geodesic interval operators, M-fuzzifying geodesic convexities can be induced.

In this paper, we first study the notion of almost convergence of double sequences of fuzzy numbers using the idea of Pringsheim’s convergence and compare with the set of bounded double sequences of fuzzy numbers. The primary aim is to characterize matrix classes involving some sets of double sequences of fuzzy numbers and also the set of almost convergent double sequence of fuzzy numbers. The approach adopted in the investigation of main results should be helpful to develop theories to deal with fuzzification of summability theory and its applications.

The selection of feature genes with high recognition ability from the gene expression profiles have gained great significances in biology. However, most of the existing methods for feature genes selection have a high time complexity where lead to a poor performance. Motivated by this, an effective feature selection method, called Fisher transformation (FT), is proposed which based on the improved Fisher discriminant analysis (FDA) and neighborhood rough set algorithms. The FT method has two benefits: 1. The multiple neighborhood rough set algorithm is used for solving the small sample size problem of FDA; 2. The improved FDA algorithm is used for selecting feature genes and ameliorating poor ability of classification. Furthermore, we measure the impact of the FT approach on the final selection consequence. The results obtained on four public tumor microarray datasets provide beneficial insight on both the benefits and limitations, paving the way to the exploration of new and wider feature selection programs.

In recent years, due window assignment scheduling problems deriving from just-in-time supply chain management have been studied extensively. However, precedence constraints and uncertain processing times of jobs are rarely involved simultaneously in the studies. In this paper, a single machine due window assignment scheduling problem with uncertain processing times, precedence constraints and due window size constraints is investigated, in which the processing times of jobs are presented by fuzzy numbers. The objective is to minimize the mean value of the total earliness-tardiness penalties. An optimal polynomial time algorithm is proposed for the problem when there are no precedence constraints among jobs. Note that the problem with general precedence constraints is NP-hard. An efficient 2-approximation algorithm is proposed for the general constraint problem based on linear programming relaxation. The experimental results show that the proposed methods are effective and promising.

This paper presents a new multi-objective problem formulation based optimal placement of Phasor Measurement Units (PMUs) considering the power system observability and reliability conditions. In this proposed method, Optimal Placement of PMUs (OPP) are determined to satisfy two objectives simultaneously such as minimizing the number of PMUs for achieving the complete observability and maximizing the reliability for better operation of power systems. Since the above two objectives are conflicting in nature, Fuzzified Clustered Gravitational Search Algorithm (FCGSA) is proposed to solve Multi-objective Optimal Placement of PMUs (MOPP) problem to provide a good tradeoff solution between the competing objectives. The fuzzy membership for each objective function is designed and proposed to determine the best solution of MOPP problem. Conventional rules are applied to minimize the number of PMUs and a new rule is developed to maximize the observability as well as reliability of the power systems. It helps the system operators to make necessary remedial actions to prevent the outage of the low reliability bus. The proposed method is validated on IEEE 14, 30 and 57 bus systems. The most effective strategy of allocating the optimal number of PMUs and their locations is demonstrated by comparing its performance with other methods reported in the available literatures.

LTE-A downlink transfers data and control information from base station to mobile. To reduce the mean square error between original and estimated channel, pilot/training based channel estimation like Least Square Error (LSE) and Linear Minimum Mean Square Error (LMMSE) are ubiquitous for most wireless standards. To optimize the channel, many intelligent optimized techniques were developed. GA has no guarantee in finding global optima and high convergence time. ANN suits only linear solutions and more training period. PSO fits high dimensional space but needs more iterations. ABC has limited search space by initial solution. CS requires large resources and high computational time. To overcome these effects, an effortless Trellis Coded Firefly Optimized LMMSE based algorithm is proposed to estimate the channel. TCM has high spectral efficiency, more data rate and reduced error. FA has low complexity, easy implementation, automatic subdivision of groups to find local/global optima and ability to deal with multimodality. At SNR = 10 dB, LSE has high MSE of 10^–2, LMMSE has 15.85% reduced MSE than LSE. The previous optimized methods have MSE ranging from 10^–3 to 10^–2 but the proposed method with 64-QAM has MSE range of 10^–5 to 10^–4, which is 100 times reduced.

In this paper a dynamic tracking control of mobile robot using neural network global fast sliding mode (NN-GFSM) is presented. The proposed strategy combines two control approaches, kinematic control and dynamic control. The laws of kinematic control are based on GFSM in order to determine the adequate velocities for the system stability in finite time. The dynamic controller combines two control techniques, the GFSM to stabilize the velocities errors, and a neural network controller in order to approximate a nonlinear function and to deal the disturbances. This dynamic controller allows the robots to follow the desired trajectory even in the presence of disturbances. The designed controller is dynamically simulated by using Matlab/ Simulink and the simulations results show the efficiency and robustness of the proposed control strategy.

This paper presents a novel transform based watermarking approach using the ridgelet transform for image decomposition. This method embeds the watermark into the Fourier amplitudes of the ridgelet coefficients of the host image. The embedding weights are calculated such that the perceptual transparency is maintained and the robustness against different attacks is guaranteed. The proposed method forms a trade off function considering the similarity between the coefficients of watermarked image and those of the original image, and robustness against different attacks. The ridgelet coefficients of the watermarked image can be obtained by the minimization of this function. Experimental results demonstrate that the newly proposed method performs well in terms of perceptual transparency, and outperforms several state of the art schemes in terms of robustness against common image processing operations and most of the geometric attacks.

In paper a general complex fuzzy matrix equation Z ˜ C = W ˜ in which C is a n × p complex matrix and W ˜ is a m × p complex LR fuzzy numbers one, is investigated. A numerical procedure for calculating the solution is designed in detail and a sufficient condition for the existence of strong complex LR fuzzy solution is derived. Some examples are given to illustrate the proposed method.

This paper introduced the notions of rough filters, multi-granulation rough filters, and rough fuzzy filters in pseudo-BCI algebras and investigated some properties. First, a congruence relation was structured by a filter on pseudo-BCI algebra. Then rough filters and rough fuzzy filters were investigated. Next, the relationships between upper (lower) rough filters and upper (lower) approximations of their fuzzy homomorphic images were discussed. Furthermore, original rough filter model was extended to a multi-granulation rough filter model, where the set approximations were defined by using multi congruence relations on pseudo-BCI algebra.

The novel fuzzy parameter varying system is proposed to deal with nonlinear time-varying models. It has the advantages of the T-S fuzzy system and the linear parameter varying system. It provides a new idea for solving nonlinear time-varying control problem. In this paper, some sufficient conditions are provided to guarantee the globally asymptotically stable of the equilibrium and to synthesize a T-S state feedback control law which can stabilize the closed loop fuzzy parameter varying system. Numerical simulations verify the effectiveness of our results.