Entropy Measures in Machine Fault Diagnosis Insights and Applications (1).pdf (2.99 MB)
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journal contribution
posted on 2020-04-21, 10:36 authored by Zhiqiang Huo, Miguel Martinez-GarciaMiguel Martinez-Garcia, Eve ZhangEve Zhang, Ruqiang Yan, Lei ShuEntropy, 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.
Funding
EPSRC Grant EVES (EP/R029741/1)
History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
- Mechanical, Electrical and Manufacturing Engineering
Department
- Aeronautical and Automotive Engineering
Published in
IEEE Transactions on Instrumentation and MeasurementVolume
69Issue
6Pages
2607 - 2620Publisher
Institute of Electrical and Electronics Engineers (IEEE)Version
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher statement
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Publication date
2020-03-16Copyright date
2020ISSN
0018-9456eISSN
1557-9662Publisher version
Language
- en