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Indexed by:期刊论文

Date of Publication:2016-12-05

Journal:NEUROCOMPUTING

Included Journals:SCIE、EI、Scopus

Volume:216

Page Number:649-656

ISSN No.:0925-2312

Key Words:Wavelet de-noising; Energy entropy; Grey relational analysis; Rolling bearing; Fault diagnosis

Abstract:Rolling bearings are necessary parts in rotary machines. However, the problem of early fault diagnosis for rolling bearings is difficult to solve due to its low signal-to-noise ratio and non-linear and non-stationary signal. Based on a detailed investigation of rolling bearing vibration signals, this paper proposes a method for determining whether a fault occurs by comparing the high-frequency band power. If a fault occurs, we first de-noise the vibration signals using wavelet de-noising and then extract the fault characteristics in both the time domain and the time-frequency domain to avoid the limitations of using only one domain. Finally, the fault location is identified using the grey correlation method. According to the method application results, the recognition accuracy using the method proposed in this paper is satisfactory, proving that the method has superior performance. (C) 2016 Elsevier B.V. All rights reserved.