Adaptive monitoring scheme of stochastically failing systems under hidden degradation processes

•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 inst...

Full description

Saved in:
Bibliographic Details
Published in:Reliability engineering & system safety Vol. 221; p. 108322
Main Authors: Duan, Chaoqun, Li, Yifan, Pu, Huayan, Luo, Jun
Format: Journal Article
Language:English
Published: Barking Elsevier Ltd 01.05.2022
Elsevier BV
Subjects:
Adaptive control
Adaptive monitoring
Algorithms
Bayesian analysis
Degradation
Fault prediction
Iterative algorithms
Iterative methods
Markov chains
Markov processes
Monitoring
Optimization
Prognostics and health management (PHM)
Reliability engineering
Semi-Markov decision process (SMDP)
Stochastic models
Fault prediction
Adaptive monitoring
Prognostics and health management (PHM)
Semi-Markov decision process (SMDP)
ISSN:0951-8320, 1879-0836
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2022.108322