Search

• Development of a multi-attribute Bayesian control scheme for fault prediction. • Multivariate hidden Markov model describes the deterioration process. • Computational algorithm to optimize the multi-attribute Bayesian policy. • Development of a real case study and verification using multivariate data. Although Bayesian approaches have been utilized in engineering systems for health prognostics, very little work has been done using Bayesian methods for fault prediction of systems under multiple attributes. To address this issue, in this paper a novel multi-attribute Bayesian control chart is presented for predicting failures of hidden-state systems by jointly considering two performance measures of system operation. The system actual status is represented by a three-state multivariate hidden stochastic process with a normal state, an abnormal state, and a failure state. The working states are unobservable and failure state is observable. Based on the built hidden-state model, a fault prediction scheme integrating both system availability and cost objectives is constructed via a multi-attribute Bayesian control chart to monitor and predict impending risks of the operational systems. The Bayesian control chart alarms when the probability of impending risks reaches a certain control limit, which is optimized and determined by a computational algorithm developed in a semi-Markov decision process framework. The proposed fault prediction scheme provides an appearing feature to jointly consider multiple attributes for hidden-state systems. A real case study of mechanical generators is presented and a comparison with other Bayesian and non-Bayesian methods is also given, which demonstrates the effectiveness and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Generally, the internal states of most engineering systems are inaccessible to sensors under operational conditions, permitting indirect measurements and failures to be observed discretely. For this type of system, a class of proportional hazard (PH) models with multistate process have been proposed for performing prognostics. However, existing studies use a constant degradation mechanism with a very limited number of states, which may fail to model the actual deterioration of the system. Therefore, this article presents a novel PH model that utilizes a nonconstant degradation mechanism with a large number of states to perform health prognostics. A matrix partition-based approximation method is developed to include all the possible state transitions and to estimate important health quantities of the system, such as conditional reliability (CR), mean remaining useful life (MRUL), and remaining useful life distribution (RULD). An online prognostic scheme based on this matrix approach is presented to provide an update with the latest health quantities of the system. The proposed approach provides appealing computational features and avoids difficulties in calculating uncountable transitions for PH model-based prognostics. The effectiveness of the proposed method is demonstrated via a case of helical gearboxes under indirect monitoring and random failure conditions. [ABSTRACT FROM AUTHOR]

Most maintenance optimization models of gear systems have considered single failure mode. There have been very few papers dealing with multiple failure modes, considering mostly independent failure modes. In this paper, we present an optimal Bayesian control scheme for early fault detection of the gear system with dependent competing risks. The system failures include degradation failure and catastrophic failure. A three‐state continuous‐time–homogeneous hidden Markov model (HMM), namely the model with unobservable healthy and unhealthy states, and an observable failure state, describes the deterioration process of the gear system. The condition monitoring information as well as the age of the system are considered in the proposed optimal Bayesian maintenance policy. The objective is to maximize the long‐run expected average system availability per unit time. The maintenance optimization model is formulated and solved in a semi‐Markov decision process (SMDP) framework. The posterior probability that the system is in the warning state is used for the residual life estimation and Bayesian control chart development. The prediction results show that the mean residual lives obtained in this paper are much closer to the actual values than previously published results. A comparison with the Bayesian control chart based on the previously published HMM and the age‐based replacement policy is given to illustrate the superiority of the proposed approach. The results demonstrate that the Bayesian control scheme with two dependent failure modes can detect the gear fault earlier and improve the availability of the system. [ABSTRACT FROM AUTHOR]

• Development of an adaptive monitoring scheme for fault detection. • Multivariate hidden Markov model describes the deterioration process. • SMDP approach to formulate the control problem and to optimize the scheme. • Considerably better fault detection compared with published results. Limited by the installation of sensors and the structure of systems, only external observation information can generate an association with the internal health status of a system. In addition, modern industrial systems are often accompanied by hidden degradation processes, which present great challenges when monitoring and alerting impending failures. To address this issue, this paper proposes an adaptive monitoring scheme for predicting faults of systems with hidden degradation processes. A multivariate, continuous-time hidden Markov model with three states (hidden healthy state, hidden unhealthy state, and observable failure state) is established to describe the degradation process of the system. An expectation maximization (EM) algorithm is presented to estimate the parameters of the stochastic model. On the basis of the hidden degradation model, an adaptive Bayesian control scheme is developed for monitoring the potential risk of the system with two switchable sampling intervals. We calculate optimal decision variables of the control scheme to achieve the minimum expected average cost by a policy iteration algorithm under a semi-Markov decision process (SMDP). The performance and characteristics of the proposed scheme are demonstrated via real oil measurement data obtained from mechanical generators. A comparison case illustrates the superiority of the prediction performance of our monitoring scheme. [ABSTRACT FROM AUTHOR]