yes / This paper presents a methodology for fault detection, fault prediction and fault isolation based on the
integration of hidden Markov modelling (HMM) and Bayesian networks (BN). This addresses the nonlinear
and non-Gaussian data characteristics to support fault detection and prediction, within an explainable hybrid
framework that captures causality in the complex engineered system. The proposed methodology is based
on the analysis of the pattern of similarity in the log-likelihood (LL) sequences against the training data for
the mixture of Gaussians HMM (MoG-HMM). The BN model identifies the root cause of
detected/predicted faults, using the information propagated from the HMM model as empirical evidence.
The feasibility and effectiveness of the presented approach are discussed in conjunction with the application
to a real-world case study of an automotive exhaust gas Aftertreatment system. The paper details the
implementation of the methodology to this case study, with data available from real-world usage of the
system. The results show that the proposed methodology identifies the fault faster and attributes the fault
to the correct root cause. While the proposed methodology is illustrated with an automotive case study, its
applicability is much wider to the fault detection and prediction problem of any similar complex engineered
system.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/18518 |
| Date | 07 June 2021 |
| Creators | Soleimani, Morteza, Campean, Felician, Neagu, Daniel |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Accepted manuscript |
| Rights | © 2021 Elsevier. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license. |
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