期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 139, 期 -, 页码 -出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2019.106585
关键词
Anomaly detection; Clustering; Fleet monitoring; Condition monitoring; Electrical motors
资金
- VLAIO (Flemish Innovation & Entrepreneurship) through the Baekeland PhD mandate [HBC.2017.0226]
- O&O project REFLEXION [IWT. 150334]
- KU Leuven research funds [C14/17/070]
- Research Foundation Flanders [EOS] [30992574]
- Flemish Government
Machine failures decrease up-time and can lead to extra repair costs or even to human casualties and environmental pollution. Recent condition monitoring techniques use artificial intelligence in an effort to avoid time-consuming manual analysis and handcrafted feature extraction. Many of these only analyze a single machine and require a large historical data set. In practice, this can be difficult and expensive to collect. However, some industrial condition monitoring applications involve a fleet of similar operating machines. In most of these applications, it is safe to assume healthy conditions for the majority of machines. Deviating machine behavior is then an indicator for a machine fault. This work proposes an unsupervised, generic, anomaly detection framework for fleet-based condition monitoring. It uses generic building blocks and offers three key advantages. First, a historical data set is not required due to online fleet-based comparisons. Second, it allows incorporating domain expertise by user-defined comparison measures. Finally, contrary to most black-box artificial intelligence techniques, easy interpretability allows a domain expert to validate the predictions made by the framework. Two use-cases on an electrical machine fleet demonstrate the applicability of the framework to detect a voltage unbalance by means of electrical and vibration signatures. (C) 2020 Elsevier Ltd. All rights reserved.
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