Entropy Measures in Machine Fault Diagnosis: Insights and Applications

Huo, Zhiqiang, Martínez-García, Miguel, Zhang, Yu, Yan, Ruqiang and Shu, Lei (2020) Entropy Measures in Machine Fault Diagnosis: Insights and Applications. IEEE Transactions on Instrumentation & Measurement . ISSN 0018-9456

Full content URL: https://doi.org/10.1109/TIM.2020.2981220

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Abstract

Entropy, as a complexity measure, has been widely applied for time series analysis. One preeminent example is the design of machine condition monitoring and industrial fault diagnostic systems.
The occurrence of failures in a machine will typically lead to non-linear characteristics in the measurements, caused by instantaneous variations, which can increase the complexity in the system response. Entropy measures are suitable to quantify such dynamic changes in the underlying process, distinguishing between different system conditions.
However, notions of entropy are defined differently in various contexts (e.g., information theory and dynamical systems theory), which may confound researchers in the applied sciences. In this paper, we have systematically reviewed the theoretical development of some fundamental entropy measures and clarified the relations among them. Then, typical entropy-based applications of machine fault diagnostic systems are summarized. Further, insights into possible applications of the entropy measures are explained, as to where and how these measures can be useful towards future data-driven fault diagnosis methodologies. Finally, potential research trends in this area are discussed, with the intent of improving online entropy estimation and expanding its applicability to a wider range of intelligent fault diagnostic systems.

Keywords:entropy, time series complexit analysis, fault diagnosis, reliability analysis, machine learning
Subjects:H Engineering > H100 General Engineering
G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Engineering
ID Code:40178
Deposited On:11 Mar 2020 09:56

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