A framework to automate fault detection and diagnosis based on moving window principal component analysis and Bayesian network

Arthur Henrique de Andrade Melani, Miguel Angelo de Carvalho Michalski, da Silva, Renan Favarão and Gilberto Francisco Martha de Souza

Reliability Engineering and System Safety, 2021, vol. 215, issue C

Abstract: Through Condition-Based Maintenance strategy, planners can monitor the health of the machinery and recommend actions based on the information obtained. Nevertheless, this approach depends on the successful establishment of Fault Detection and Diagnosis (FDD) processes. Although FDD is a research area in full growth with the development of several methods and heuristics, the availability of data from systems under a fault condition is still scarce in many applications, mainly related to complex systems. In many circumstances, only data from the system in healthy conditions is available and the applied FDD method should be able to detect variations in system conditions and diagnose faults without the need for previous labeled fault data. In this context, this article proposes a hybrid framework to automate FDD based on Moving Window Principal Component Analysis (MWPCA) and Bayesian Network (BN). First, the knowledge base on technical systems is organized to support the next steps of the framework. Then, the detection and diagnosis processes are performed sequentially through MWPCA and BN. The framework was implemented in the analysis of a simplified model of a hydrogenerator, considering real and simulated data. The results showed that the proposed method was able to detect and diagnose several simulated failures.

Keywords: Fault detection and diagnosis; Principal component analysis; MWPCA; Adaptative principal component analysis; Bayesian network (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021003574

DOI: 10.1016/j.ress.2021.107837

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