期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 163, 期 -, 页码 -出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2021.108105
关键词
Anomaly detection; Explainable artificial intelligence; Fault detection; Fault diagnosis; Rotating machinery; Condition monitoring
资金
- Brazilian research funding agency CNPq (National Council for Scientific and Techno-logical Development)
- Brazilian research funding agency CAPES (Federal Agency for the Support and Improvement of Higher Education)
This paper introduces a new approach for fault detection and diagnosis in rotating machinery, which includes feature extraction, fault detection, and fault diagnosis. Fault detection is achieved through vibration feature extraction and anomaly detection algorithms, while fault diagnosis is performed using the SHAP technique for interpretation of black-box models.
The monitoring of rotating machinery is an essential task in today's production processes. Currently, several machine learning and deep learning-based modules have achieved excellent results in fault detection and diagnosis. Nevertheless, to further increase user adoption and diffusion of such technologies, users and human experts must be provided with explanations and insights by the modules. Another issue is related, in most cases, with the unavailability of labeled historical data that makes the use of supervised models unfeasible. Therefore, a new approach for fault detection and diagnosis in rotating machinery is here proposed. The methodology consists of three parts: feature extraction, fault detection and fault diagnosis. In the first part, the vibration features in the time and frequency domains are extracted. Secondly, in the fault detection, the presence of fault is verified in an unsupervised manner based on anomaly detection algorithms. The modularity of the methodology allows different algorithms to be implemented. Finally, in fault diagnosis, Shapley Additive Explanations (SHAP), a technique to interpret black-box models, is used. Through the feature importance ranking obtained by the model explainability, the fault diagnosis is performed. Two tools for diagnosis are proposed, namely: unsupervised classification and root cause analysis. The effectiveness of the proposed approach is shown on three datasets containing different mechanical faults in rotating machinery. The study also presents a comparison between models used in machine learning explainability: SHAP and Local Depth-based Feature Importance for the Isolation Forest (Local-DIFFI). Lastly, an analysis of several state-ofart anomaly detection algorithms in rotating machinery is included.
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