Entropy Measures in Machine Fault Diagnosis Insights and Applications (1).pdf (2.99 MB)
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Entropy measures in machine fault diagnosis: insights and applications

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journal contribution
posted on 21.04.2020, 10:36 by Zhiqiang Huo, Miguel Martinez-GarciaMiguel Martinez-Garcia, Eve ZhangEve Zhang, Ruqiang Yan, Lei Shu
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.

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 Measurement

Volume

69

Issue

6

Pages

2607 - 2620

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher 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

2020-03-16

Copyright date

2020

ISSN

0018-9456

eISSN

1557-9662

Language

en

Depositor

Dr Eve Zhang. Deposit date: 16 April 2020

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