Mechanical Fault Classification and Root Cause analysis Based on Machine Learning and Explainable AI

Ya Ting Lee, Hung Kai Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study utilizes machine learning and explainable AI (XAI) to construct accurate prediction models and root cause analysis for Prognostic and Health Management (PHM). The study proposes a novel research framework consisting of two stages: the first stage involves fault type classification, while the second stage involves fault severity classification and root cause analysis. In both stages, multiple machine learning classifiers are employed to identify faults in rotating machinery, utilizing vibration features associated with the rotation frequency and its harmonics for classification. XAI techniques are incorporated for feature extraction of important features and root cause analysis. The proposed approach is evaluated using the Machinery Fault Database (MAFAULDA), and experimental results show that the first stage achieves an average accuracy of 98.7%, better than results reported in the literature. The second stage achieves an average accuracy of 86.7%. The results indicate that XAI feature importance improves classification accuracy and reduces computational costs and time. Additionally, XAI-based root cause analysis provides a faster way to identify the type and severity of faults through important features and setting thresholds determining the root cause of process variations.

Original languageEnglish
Title of host publication28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023
PublisherInternational Society of Science and Applied Technologies
Pages211-215
Number of pages5
ISBN (Electronic)9798986576121
Publication statusPublished - 2023
Event28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023 - San Francisco, United States
Duration: 2023 Aug 32023 Aug 5

Publication series

Name28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023

Conference

Conference28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023
Country/TerritoryUnited States
CitySan Francisco
Period23-08-0323-08-05

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality

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