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

Ya Ting Lee, Hung Kai Wang

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023
發行者International Society of Science and Applied Technologies
頁面211-215
頁數5
ISBN(電子)9798986576121
出版狀態Published - 2023
事件28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023 - San Francisco, United States
持續時間: 2023 8月 32023 8月 5

出版系列

名字28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023

Conference

Conference28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023
國家/地區United States
城市San Francisco
期間23-08-0323-08-05

All Science Journal Classification (ASJC) codes

  • 安全、風險、可靠性和品質

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