Remaining useful life (RUL) prediction is a reliable tool for the health management of components. The main concern of RUL prediction is how to accurately predict the RUL under uncertainties. In order to enhance the prediction accuracy under uncertain conditions, the relevance vector machine (RVM) is extended into the probability manifold to compensate for the weakness caused by evidence approximation of the RVM. First, tendency features are selected based on the batch samples. Then, a dynamic multistep regression model is built for well describing the influence of uncertainties. Furthermore, the degradation tendency is estimated to monitor degradation status continuously. As poorly estimated hyperparameters of RVM may result in low prediction accuracy, the established RVM model is extended to the probabilistic manifold for estimating the degradation tendency exactly. The RUL is then prognosticated by the first hitting time (FHT) method based on the estimated degradation tendency. The proposed schemes are illustrated by a case study, which investigated the capacitors' performance degradation in traction systems of high-speed trains.

In order to achieve fault diagnosis and prognosis, one needs a sufficient and valid life-cycle data. However, this requirement is difficult for current high-reliable manufacturing system. Good thing is that the technique of accelerated degradation testing can be used to address this issue. Bad thing is that, it needs a reliable testing/measuring technique to build an accurate model for accelerated degradation testing. However, in practical applications, data acquisition is obtained by sensors or measurement devices, which cannot guarantee perfect working condition under the influence of external environment and stresses, resulting in unreliable signals. Furthermore, since traditional models require complex differentiation and cannot obtain analytical expressions when considering unreliable signals, traditional models rarely reflect well this situation. Motivated by these facts, an accurate model for the accelerated degradation testing is proposed in this study with considering the unreliable signals. Based on the proposed model, a closed-form expression for the useful life analysis is derived. The M-H sampling method is used to estimate the unknown parameters used in the proposed model. For illustration, the electrical connectors dataset is analyzed with the proposed model and the traditional models. Comparing the obtained results, the proposed model is more accurate in the useful life analysis than the traditional accelerated degradation testing models by considering the unreliable signals.

This study investigates the distributed fault-tolerant consensus issue of multi-agent systems subject to complicated abrupt and incipient time-varying actuator faults in physical hierarchy and aperiodic denial-of-service (DoS) attacks in networked hierarchy. Decentralized estimators are devised to estimate consecutive system states and actuator faults. A unified framework with an absolute local output-based closed-loop estimator in decentralized fault estimation design and a relative broadcasting state-based open-loop estimator in distributed event-triggered fault-tolerant consensus design is developed. Criteria of exponential consensus of the faulty multi-agent systems under DoS attacks are derived by virtue of average dwelling time and attack frequency technique. Simulations are outlined to confirm the efficacy of the proposed distributed fault-tolerant consensus control algorithm based on an event-triggered mechanism.

Accurate fault detection and isolation (FDI) relies on the information collection. This can be done by the optimal test selection which can also reduce the life cost of engineering systems. In recent years, some researchers have made lots of achievement on solving the test selection design problem. However, few of them concerned a method to deal with the ambiguity problem caused by the multiple fault modes. In this article, a data-driven based method for test selection is proposed to build an accurate test selection design model. Then, we propose a copula function on cross validation-based Kullback-Leibler (CCVKL) divergence method to construct an accurate constraint model. An improved discrete binary particle swarm optimization algorithm is used to obtain the optimal test design solution. The proposed method-also in comparison to three other existing methods are performed in an electrical circuit.

Data-driven fault detection and isolation (FDI) depends on complete, comprehensive, and accurate fault information. Optimal test selection can substantially improve information achievement for FDI and reduce the detecting cost and the maintenance cost of the engineering systems. Considerable efforts have been worked to model the test selection problem (TSP), but few of them considered the impact of the measurement uncertainty and the fault occurrence. In this article, a conditional joint distribution (CJD)-based test selection method is proposed to construct an accurate TSP model. In addition, we propose a deep copula function which can describe the dependency among the tests. Afterward, an improved discrete binary particle swarm optimization (IBPSO) algorithm is proposed to deal with TSP. Then, application to an electrical circuit is used to illustrate the efficiency of the proposed method over two available methods: 1) joint distribution-based IBPSO and 2) Bernoulli distribution-based IBPSO.