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Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China
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Entropy 2019, 21(11), 1061; https://doi.org/10.3390/e21111061
Received: 16 September 2019 / Revised: 25 October 2019 / Accepted: 27 October 2019 / Published: 29 October 2019
Rotating machinery plays an important role in various kinds of industrial engineering. How to assess their conditions is a key problem for operating safety and condition-based maintenance. The potential anomaly, fault and failure information can be obtained by analyzing the collected condition monitoring data of the previously deployed sensors in rotating machinery. Among the available methods of analyzing sensors data, entropy and its variants can provide quantitative information contained in these sensing data. For implementing fault detection, diagnosis, and prognostics, this information can be utilized for feature extraction and selecting appropriate training data for machine learning methods. This article aims to review the related entropy theories which have been applied for condition monitoring of rotating machinery. This review consists of typical entropy theories presentation, application, summary, and discussion. View Full-Text
Keywords: entropy theory; feature extraction; fault detection; fault diagnosis; fault prognostics; rotating machinery entropy theory; feature extraction; fault detection; fault diagnosis; fault prognostics; rotating machinery
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Liu, L.; Zhi, Z.; Zhang, H.; Guo, Q.; Peng, Y.; Liu, D. Related Entropy Theories Application in Condition Monitoring of Rotating Machineries. Entropy 2019, 21, 1061.

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