The detection of multiple faults in a Bayesian setting using dynamic programming approaches
©2020 Elsevier B.V. Inspired by the need for improving the reliability and safety of complex dynamic systems, this paper tackles the multiple faults detection problem using Dynamic Programming (DP) based methods under the Bayesian framework. These methods include (i) Maximum-A-Posteriori (MAP) es...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | English |
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ELSEVIER
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/80596 |
| _version_ | 1848764242008735744 |
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| author | Habibi, Hamed Howard, Ian Habibi, R. |
| author_facet | Habibi, Hamed Howard, Ian Habibi, R. |
| author_sort | Habibi, Hamed |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | ©2020 Elsevier B.V.
Inspired by the need for improving the reliability and safety of complex dynamic systems, this paper tackles the multiple faults detection problem using Dynamic Programming (DP) based methods under the Bayesian framework. These methods include (i) Maximum-A-Posteriori (MAP) estimator approach, (ii) Monte Carlo Markov Chain (MCMC) posteriors, (iii) Set Membership (SM) approach, (iv) probability of fault and (v) alternative methods. Using Bernoulli and Poisson priors, the Bayesian DP-type MAP estimate of all unknown parameters is presented. To derive the posterior distributions of Bayesian point estimations, the MCMC method is applied. For the SM approach, the Bayesian feasible parameter space is derived, as Bayesian confidence interval. The SM criteria are proposed to detect multiple faults which also reduces the Bayesian complexity of MAP estimator. For online fault detection, using the Bayesian model selection technique and the MAP estimator, the DP-based probability of faults is given, serving as a Bayesian early warning system. Since running DP algorithms is a time-consuming, alternative methods are also proposed using the modified MAP estimator. These methods use iterative approximations of MAP estimates, via the application of an iterative Expectation–Maximization algorithm technique. Numerical simulations are conducted and analysed to evaluate the performance of the proposed methods. |
| first_indexed | 2025-11-14T11:16:14Z |
| format | Journal Article |
| id | curtin-20.500.11937-80596 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:16:14Z |
| publishDate | 2020 |
| publisher | ELSEVIER |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-805962022-06-03T08:35:05Z The detection of multiple faults in a Bayesian setting using dynamic programming approaches Habibi, Hamed Howard, Ian Habibi, R. Science & Technology Technology Engineering, Electrical & Electronic Engineering Bayesian posterior Bayesian model selection Dynamic programming Early warning system Iterative MAP MCMC Probability of fault Set Membership SET-MEMBERSHIP IDENTIFICATION CHANGE-POINTS BINARY SEGMENTATION SYSTEMS ©2020 Elsevier B.V. Inspired by the need for improving the reliability and safety of complex dynamic systems, this paper tackles the multiple faults detection problem using Dynamic Programming (DP) based methods under the Bayesian framework. These methods include (i) Maximum-A-Posteriori (MAP) estimator approach, (ii) Monte Carlo Markov Chain (MCMC) posteriors, (iii) Set Membership (SM) approach, (iv) probability of fault and (v) alternative methods. Using Bernoulli and Poisson priors, the Bayesian DP-type MAP estimate of all unknown parameters is presented. To derive the posterior distributions of Bayesian point estimations, the MCMC method is applied. For the SM approach, the Bayesian feasible parameter space is derived, as Bayesian confidence interval. The SM criteria are proposed to detect multiple faults which also reduces the Bayesian complexity of MAP estimator. For online fault detection, using the Bayesian model selection technique and the MAP estimator, the DP-based probability of faults is given, serving as a Bayesian early warning system. Since running DP algorithms is a time-consuming, alternative methods are also proposed using the modified MAP estimator. These methods use iterative approximations of MAP estimates, via the application of an iterative Expectation–Maximization algorithm technique. Numerical simulations are conducted and analysed to evaluate the performance of the proposed methods. 2020 Journal Article http://hdl.handle.net/20.500.11937/80596 10.1016/j.sigpro.2020.107618 English http://creativecommons.org/licenses/by-nc-nd/4.0/ ELSEVIER fulltext |
| spellingShingle | Science & Technology Technology Engineering, Electrical & Electronic Engineering Bayesian posterior Bayesian model selection Dynamic programming Early warning system Iterative MAP MCMC Probability of fault Set Membership SET-MEMBERSHIP IDENTIFICATION CHANGE-POINTS BINARY SEGMENTATION SYSTEMS Habibi, Hamed Howard, Ian Habibi, R. The detection of multiple faults in a Bayesian setting using dynamic programming approaches |
| title | The detection of multiple faults in a Bayesian setting using dynamic programming approaches |
| title_full | The detection of multiple faults in a Bayesian setting using dynamic programming approaches |
| title_fullStr | The detection of multiple faults in a Bayesian setting using dynamic programming approaches |
| title_full_unstemmed | The detection of multiple faults in a Bayesian setting using dynamic programming approaches |
| title_short | The detection of multiple faults in a Bayesian setting using dynamic programming approaches |
| title_sort | detection of multiple faults in a bayesian setting using dynamic programming approaches |
| topic | Science & Technology Technology Engineering, Electrical & Electronic Engineering Bayesian posterior Bayesian model selection Dynamic programming Early warning system Iterative MAP MCMC Probability of fault Set Membership SET-MEMBERSHIP IDENTIFICATION CHANGE-POINTS BINARY SEGMENTATION SYSTEMS |
| url | http://hdl.handle.net/20.500.11937/80596 |