Structural Health Monitoring

Support vector machine, chaos theory, and particle swarm optimization are combined to build the prediction model of dam safety. The approaches are proposed to optimize the input and parameter of prediction model. First, the phase space reconstruction of prototype monitoring data series on dam behavior is implemented. The method identifying chaotic characteristics in monitoring data series is presented. Second, support vector machine is adopted to build the prediction model of dam safety. The characteristic vector of historical monitoring data, which is taken as support vector machine input, is extracted by phase space reconstruction. The chaotic particle swarm optimization algorithm is introduced to determine support vector machine parameters. A chaotic support vector machine–based prediction model of dam safety is built. Finally, the displacement behavior of one actual dam is taken as an example. The prediction capability on the built prediction model of dam displacement is evaluated. It is indicated that the proposed chaotic support vector machine–based model can provide more accurate forecasted results and is more suitable to be used to identify efficiently the dam behavior.

This article describes how piezoelectric transducers embedded in the adhesive bond-line of lap-joints can be used to effectively monitor structural integrity. Various lap-joint coupons with embedded piezoelectric transducers were manufactured with and without artificial contamination at the bond-line and tested statically and cyclically. A novel scheme based on the electromechanical impedance response of the transducer was implemented to predict the failure of the tested lap-joint samples. The results from the mechanical testing indicated that monitoring the transducer’s electromechanical impedance is an effective way of predicting the failure of the bond-line. Specifically for static tests, local damage to the bond-line was consistently detected at approximately 84% of the failure load for transducers located at the center of the bond-line, whereas for transducers embedded near the edge of the bond-line, the failure of the adhesive was detected at 60% of the failure load. Moreover, preliminary fatigue tests showed that significant changes in the electromechanical impedance signals were apparent starting at 60% of the life of the bond-line. In addition to the mechanical testing, the effectiveness of the proposed electromechanical impedance–based scheme was investigated by means of a three-dimensional finite element model corresponding to the specific coupon geometry tested and through a two-dimensional analytical solution.

Extracting damage-sensitive features plays an important role in all structural health monitoring applications, as it determines the metrics on which to base decision-making with regard to operation, maintenance, damage state, and so on. This article adopts the widely employed frequency response function, both its magnitude and phase, as the selected feature source and demonstrates how the damage types and locations are able to be classified by means of Bayesian recursive confidence updating. The features are estimated from the in situ acquired vibration data on a rotating machinery test-bed, and the probabilistic models that quantify feature uncertainty are the likelihood functions in a Bayesian framework, which informs the most plausible decisions based on the collected evidence. The damage classification effort in this article specifically calculates the posterior probability, considering the prior and likelihood of data observations; posterior probabilities are then fed back as prior probabilities in the next iteration as new test data are observed. There are three ball-bearing damage conditions applied to the rotary machine test-bed, and the correct model representing the correct damage types will be selected by the model with the maximum posterior confidence. Classification via posterior probability is shown in this article to outperform traditional likelihood evaluations, and the Bayesian recursive implementation distinguishes all three conditions in this work.

The central message of this article is that for robust and efficient damage detection the damage sensitive features should be selected optimally in a systematic way such that only these features that contribute the most to damage detectability be retained. Furthermore, suitable transformations of the original features may also enhance damage detectability. We explore these principles using data from a wind turbine blade. Several damage extent scenarios are introduced non-destructively. Partial autocorrelation coefficients are proposed as vibration-based damage sensitive features. Scores calculated with principal component analysis of partial autocorrelation coefficients are the transformed damage sensitive features. Statistical distances between the damage sensitive feature subsets estimated from the healthy and a reference damage state are calculated with respect to a statistical threshold as a measure of optimality. The fast forward method and a genetic algorithm are used to optimize the detectability of damage by damage sensitive feature selection. The comparison between the two methods points out that fast forward offers a comparable performance at a lower computational cost. The classifiers based on the optimal features are tested on data from several previously unseen healthy and damaged cases and across a range of statistical detection thresholds. It is demonstrated that the selected principal component analysis scores of the partial autocorrelation coefficients are superior compared to the initial features and allow identifying small damage confidently.

The potential for increased productivity offered by automated fibre placement method has opened up a wider range of applications as well as new markets for composite materials. However, like many other manufacturing methods, different flaws such as voids or delamination may still occur during or after lay-up. Therefore, the use of automated fibre placement as an open-mould process where fibre/tape material is fed brings with it a need and an opportunity to establish a reliable inspection and monitoring method to ensure structural integrity not only after fabrication but also one step earlier, during the manufacturing process. Since optical fibre–based photonic sensing technologies are increasingly common for structural health monitoring of composite structures, selection of optical fibre Bragg grating sensors for manufacturing process monitoring has been successfully implemented here. Experiments are carried out on glass fibre/high-density polyethylene laminates with embedded fibre Bragg grating in the automated fibre placement method. The lay-up process conditions are monitored by the fibre Bragg grating sensors via measuring the reflected wavelengths which are related to pressure and temperature. The results presented in this article indicate that fibre Bragg grating sensing technique can be reliably employed for online monitoring of lay-up process to ensure the quality of final product.

One outstanding obstacle that hinders robust application of vibration-based damage identification to civil structures is that the number of sensors installed on a large civil structure is always limited, compared with the total degrees of freedom of the structure, so that the limited measured responses may not provide enough information for detecting local damage. Furthermore, developments in sensor technology make installation of heterogeneous sensors on a structure practical and feasible while every type of sensor has its own merits and drawbacks for damage identification. But the benefits of utilizing heterogeneous sensors in vibration-based damage identification have not been fully investigated. This study proposes a damage identification method by combining the response reconstruction technique with the response sensitivity–based finite element model updating method to address these issues. The number and location of heterogeneous sensors, such as accelerometers, displacement transducers, and strain gauges, are optimally and collectively determined in an optimization strategy to obtain the best reconstruction of multi-type responses of a structure using Kalman filter. After damage occurrence, radial basis function network is employed to predict the mode shapes using the modal properties extracted from the measurement data by experimental modal analysis method, and these modal properties are further used to reconstruct responses of the damaged structure. The reconstructed responses are finally used to identify the damage in terms of sensitivity-based finite element model updating. In every updating, the sparse regularization is employed to increase the identification accuracy. A simply supported overhanging steel beam composed of 40 elements serves as a numerical study to demonstrate the procedure and feasibility of the proposed method. The validation of this method is further conducted by laboratory test. Both simulation study and laboratory test show that the multi-sensing approach via response reconstruction does improve the identification accuracy of damage location and quantization considerably.

Wind-induced operational variability is one of the major challenges for structural health monitoring of slender engineering structures like aircraft wings or wind turbine blades. Damage sensitive features often show an even bigger sensitivity to operational variability. In this study a composite cantilever was subjected to multiple mass configurations, velocities and angles of attack in a controlled wind tunnel environment. A small-scale impact damage was introduced to the specimen and the structural response measurements were repeated. The proposed damage detection methodology is based on automated operational modal analysis. A novel baseline preparation procedure is described that reduces the amount of user interaction to the provision of a single consistency threshold. The procedure starts with an indeterminate number of operational modal analysis identifications from a large number of datasets and returns a complete baseline matrix of natural frequencies and damping ratios that is suitable for subsequent anomaly detection. Mahalanobis distance-based anomaly detection is then applied to successfully detect the damage under varying severities of operational variability and with various degrees of knowledge about the present operational conditions. The damage detection capabilities of the proposed methodology were found to be excellent under varying velocities and angles of attack. Damage detection was less successful under joint mass and wind variability but could be significantly improved through the provision of the currently encountered operational conditions.