Proceedings of the Institution of Mechanical Engineers,Part O: Journal of Risk and Reliability

Predict the remaining useful life of devices in real time is important to maintenance management. This article addresses a remaining useful life predictive problem where the considered device suffers from non-stationary degradation with shocks. A novel degradation-shock system model subjected to a hidden degradation state is proposed to characterize the degradation process. To estimate the hidden system state, the Kalman filter is modified with optimal estimation. Then, based on Bayes’ theorem, a recursive algorithm is presented to estimate the unknown parameters of the model. With the proposed approach, the hidden state and unknown parameters can be updated at each sampling instant. Subsequently, the analytical solution for the remaining useful life is obtained, while the characteristics of the shocks as well as the uncertainties of the estimated system state are taken into account. Furthermore, the effectiveness of the proposed model is verified by a numerical simulation and a practical case study on a milling machine.

In this article, a parametric model for health condition monitoring of wind turbines is developed. The study is based on the assumption that a wind turbine’s health condition can be modeled through three features: rotor speed, gearbox temperature and generator winding temperature. At first, three neural network models are created to simulate normal behavior of each feature. Deviation signals are then defined and calculated as accumulated time-series of differences between neural network predictions and actual measurements. These cumulative signals carry health condition–related information. Next, through nonlinear regression technique, the signals are used to produce individual models for considered features, which mathematically have the form of proportional hazard models. Finally, they are combined to construct an overall parametric health condition model which partially represents health condition of the wind turbine. In addition, a dynamic threshold for the model is developed to facilitate and add more insight in performance monitoring aspect. The health condition monitoring of wind turbine model has capability of evaluating real-time and overall health condition of a wind turbine which can also be used with regard to maintenance in electricity generation in electric power systems. The model also has flexibility to overcome current challenges such as scalability and adaptability. The model is verified in illustrating changes in real-time and overall health condition with respect to considered anomalies by testing through actual and artificial data.

In this article, we present a maintenance model for metropolitan train wheels subjected to diameter or flange thickness overruns that includes condition monitoring with periodic inspection. We present a dynamic ( x θ , r ) policy based on condition monitoring information, where x θ is the wheel flange thickness threshold that triggers preventive re-profiling and r is the recovery value for the wheel flange thickness after preventive re-profiling. The problem is modelled as a semi-Markov decision process that considers wear in terms of the diameter and flange thickness simultaneously. The problem is formulated in a two-dimensional state space; this space is defined as a combination of the diameter state and the flange thickness state. The model also considers imperfect wheel maintenance. The model’s objective is to minimize the maintenance cost per unit time that is expected in the long run. We apply a policy-iteration algorithm as the computational approach to determine the optimal re-profiling policy and use an example to demonstrate the method’s effectiveness.

In this article, a novel approach, that is, convex model method of set theory, is proposed to investigate the non-probabilistic reliability of bridge crane. Considering the metal structure system of the bridge crane, the finite element method is applied to obtain the stress response of the structure dangerous point. Then, the sample of stress response of the structure danger point and uncertain parameters are obtained. Finally, based on support vector machines, the structure implicit regression function of the system is replaced by explicit expression that calculates the non-probabilistic reliability of the structure. Results show that this approach is useful and efficient to solve the problem of non-probabilistic reliability in the metal structure.

The maintenance optimization of complex systems is a key question. One important objective is to be able to anticipate future maintenance actions required to optimize the logistic and future investments. That is why, over the past few years, the predictive maintenance approaches have been an expanding area of research. They rely on the concept of prognosis. Many papers have shown how dynamic Bayesian networks can be relevant to represent multicomponent complex systems and carry out reliability studies. The diagnosis and maintenance group from French institute of science and technology for transport, development and networks (IFSTTAR) developed a model (VirMaLab: Virtual Maintenance Laboratory) based on dynamic Bayesian networks in order to model a multicomponent system with its degradation dynamic and its diagnosis and maintenance processes. Its main purpose is to model a maintenance policy to be able to optimize the maintenance parameters due to the use of dynamic Bayesian networks. A discrete state-space system is considered, periodically observable through a diagnosis process. Such systems are common in railway or road infrastructure fields. This article presents a prognosis algorithm whose purpose is to compute the remaining useful life of the system and update this estimation each time a new diagnosis is available. Then, a representation of this algorithm is given as a dynamic Bayesian network in order to be next integrated into the Virtual Maintenance Laboratory model to include the set of predictive maintenance policies. Inference computation questions on the considered dynamic Bayesian networks will be discussed. Finally, an application on simulated data will be presented.

This article presents a case study determining the optimal preventive maintenance policy for a light rail rolling stock system in terms of reliability, availability, and maintenance costs. The maintenance policy defines one of the three predefined preventive maintenance actions at fixed time-based intervals for each of the subsystems of the braking system. Based on work, maintenance, and failure data, we model the reliability degradation of the system and its subsystems under the current maintenance policy by a Weibull distribution. We then analytically determine the relation between reliability, availability, and maintenance costs. We validate the model against recorded reliability and availability and get further insights by a dedicated sensitivity analysis. The model is then used in a sequential optimization framework determining preventive maintenance intervals to improve on the key performance indicators. We show the potential of data-driven modelling to determine optimal maintenance policy: same system availability and reliability can be achieved with 30% maintenance cost reduction, by prolonging the intervals and re-grouping maintenance actions.

Risk analysis of concrete dams and quantification of the failure probability are important tasks in dam safety assessment. The conditional probability of demand and capacity is usually estimated by numerical simulation and Monte Carlo technique. However, the estimated failure probability (or the reliability index) is dam-dependent which makes its application limited to some case studies. This article proposes an analytical failure model for generic gravity dam classes which is optimized based on large number of nonlinear finite element analyses. A hybrid parametric–probabilistic–statistical approach is used to estimate the failure probability as a function of dam size, material distributional models and external hydrological hazard. The proposed model can be used for preliminary design and evaluation of two-dimensional gravity dam models.

The satisfaction of client needs is the goal of most of the industrial systems, which can be achieved by appropriate life-cycle management. When it concerns complex real-world systems, the difficulty of managing their life-cycle increases with increasing of the links and interactions between the system components and between the system and its environment. Therefore, the need of addressing a complete and realistic maintenance planning approach to face these difficulties is crucial. This article presents a methodology for maintenance optimization of complex systems using Bayesian networks. In this methodology, the objective function, which aims at maximizing the system benefit, allows conciliating between two contradictory objectives: reducing the maintenance costs and reaching an availability target fixed according to the customer demand. The Bayesian networks are used to take into account the system interactions, while the maintenance policy, which is based on the imperfect preventive maintenance and considers several efficiency levels, is used to build a realistic maintenance planning model. An application to a water supply system is included to illustrate the benefit and the effectiveness of the proposed approach.

Identifying the parameters that substantially affect the time-dependent reliability is critical for reliability-based design of motion mechanism. The time-dependent local reliability sensitivity and global reliability sensitivity are the two effective techniques for this type of analysis. This work extends the first-passage method and PHI2 method, which are commonly used for estimating the time-dependent reliability, for efficiently estimating the time-dependent local reliability sensitivity and global reliability sensitivity indices of the motion mechanism. Both the local reliability sensitivity and global reliability sensitivity indices are analytically derived based on the Poisson assumption–based first-passage method and the first-order Taylor’s expansion of the motion error function. Compared with the current envelope function method for estimating the time-dependent local reliability sensitivity and global reliability sensitivity indices, the developed method does not need to estimate the second-order derivatives of motion error function, thus is more applicable. The accuracy and effectiveness of the proposed method are demonstrated by a numerical example and a satellite antenna, the direction of which is controlled by a four-bar function generator mechanism.

This article deals with a new redundancy allocation model for non-repairable series-parallel systems with multiple strategy choices. The proposed model simultaneously determines the type of components, number of active and standby components to maximize system reliability subject to design constraints. Traditionally, due to complexity and difficulty in obtaining the closed form version of system reliability, a convenient lower-bound on system reliability has been widely applied to approximate it. Assuming that switching mechanism time-to-failure is exponentially distributed, the closed form version of the reliability of subsystems with cold standby redundancy is derived analytically for the first time. This is successfully performed using Markov process and solving the relevant set of differential-difference equations. With respect to the obtained formulation, a semi-analytical expression for the reliability of subsystems with mixed redundancy strategy is also extracted. Component time-to-failure is assumed to follow an Erlang distribution which is suitable for most engineering design problems. The presented model is linear and in the form of standard zero-one integer programs and thus using integer programming algorithms guarantees optimal solutions. The computational results of solving a well-known example indicate the high performance of the proposed model in improving system reliability.

The number of samples available for testing of a newly developed item is quite small, and only limited reliability information is available in many real cases. Therefore, a multi-purpose test plan is essential for reliability estimation. To reflect real product development scenarios, this study presents a practical lifetime estimation strategy based on a partially step-stress-accelerated degradation test (PSSADT) with three stress levels. The PSSADT plan assumes that the degradation path follows a Wiener process and that the cumulative exposure model holds. The proposed test plan determines the stress level in the final loaded step that minimizes the asymptotic variance of the maximum likelihood estimator of the qth quantile of the lifetime distribution under the use condition. Finally, the test scenario, which includes the necessary validity check of the acceleration model, is illustrated with an example.