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A novel fault detection and diagnostic Petri net methodology for dynamic systems

Badmus, Taofeeq Alabi; Remenyte-Prescott, Rasa; Prescott, Darren

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Authors

Taofeeq Alabi Badmus



Abstract

Faults can have significant, negative impacts on the operation and performance of simple and complex dynamic systems. Based on the integration of Bayesian network diagnostic features with Petri net formalism, the existing Bayesian-supported Petri net tool has demonstrated the flexibility of using the Petri net approach for diagnosing failure scenario of a dynamic system. However, studies on using the proposed hybrid Petri net approach for condition monitoring and early detection and diagnosis of single and multiple failures in a dynamic system with feedback control loops are yet to be investigated. Thus, this paper presents a methodology to address this research gap using the operation of a water tank level control system as a case study. The method combines the constructed Generalised Stochastic Petri Net (GSPN) model of the system operation with its corresponding fault diagnostic Petri net model, created using the proposed modified Bayesian Stochastic Petri Net (mBSPN) formalism. The GSPN model establishes the causal relationships between the system’s components and/or subsystems. It further identifies deviations in the sensor measurements of the observable process variables characterising the system operation. The information provided by the sensors in the system model are then inputted into the mBSPN model to diagnose the root cause of the observed deviations. The obtained results demonstrated the capability of using the proposed integrated Petri net methodology for system condition monitoring, early fault detection and diagnosis of single and multiple failures in a dynamic system with feedback control loops.

Citation

Badmus, T. A., Remenyte-Prescott, R., & Prescott, D. (2023). A novel fault detection and diagnostic Petri net methodology for dynamic systems. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, https://doi.org/10.1177/1748006x231212539

Journal Article Type Article
Acceptance Date Oct 24, 2023
Online Publication Date Nov 29, 2023
Publication Date Nov 29, 2023
Deposit Date Oct 27, 2023
Publicly Available Date Jan 5, 2024
Journal Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Print ISSN 1748-006X
Electronic ISSN 1748-0078
Publisher SAGE Publications
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1177/1748006x231212539
Keywords Petri net; Bayesian network; fault diagnosis; dynamic system; inference algorithm
Public URL https://nottingham-repository.worktribe.com/output/26535066

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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/

Copyright Statement
© IMechE 2023. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).






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