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Entropy 2019, 21(4), 409; https://doi.org/10.3390/e21040409 (registering DOI)

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Department of Astronautical Science and Mechanics, Harbin Institute of Technology (HIT), Harbin 150001, China
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Received: 14 February 2019 / Revised: 12 March 2019 / Accepted: 12 April 2019 / Published: 17 April 2019
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis)
PDF [696 KB, uploaded 17 April 2019]
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Abstract

Rotating machinery is widely applied in various types of industrial applications. As a promising field for reliability of modern industrial systems, early fault diagnosis (EFD) techniques have attracted increasing attention from both academia and industry. EFD is critical to provide appropriate information for taking necessary maintenance actions and thereby prevent severe failures and reduce financial losses. A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings. The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based methods. Finally, a summary and some new research prospects are discussed.
Keywords: early fault diagnosis; rotating machinery; signal processing; feature extraction early fault diagnosis; rotating machinery; signal processing; feature extraction
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Wei, Y.; Li, Y.; Xu, M.; Huang, W. A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery. Entropy 2019, 21, 409.

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