@article{f77b0aaa74244d7ba421cfed3f2bdfd2,
title = "A phase linearisation–based modulation signal bispectrum for analysing cyclostationary bearing signals",
abstract = "Bearings are used as the most important load-carrying transmission components in various machines, thus subjecting to a number of faults including wear, fatigue pitting, cracks and so on. Fault detection and diagnosis of bearings can effectively prevent the machine from such typical failures and subsequent consequences. The faults in bearings can lead to the vibration signals that exhibit cyclostationary characteristics due to the inevitably random phase noise (or slippage between bearing components). In this article, a phase linearisation–based modulation signal bispectrum is proposed to tune up the cyclostationary bearing signal into a periodic waveform by linearizing the instantaneous phase of the narrow frequency band signals. In this way, the signal becomes more deterministic and modulation signal bispectrum can be effectively applied to suppression noise and obtain accurate and robust diagnosis results. As a result, this fault detector can achieve high performance in characterising the nonstationary bearing vibration signals and hence diagnose the bearing faults even in the case of extremely low signal-to-noise ratio (<−20 dB), which is benchmarked by the method of conventional modulation signal bispectrum in both simulation and experiment studies.",
keywords = "Cyclostationary signals, Fault diagnosis, Modulation signal bispectrum, Phase linearisation, Rolling bearings, modulation signal bispectrum, fault diagnosis, phase linearisation, cyclostationary signals",
author = "Yuandong Xu and Chao Fu and Ning Hu and Baoshan Huang and Fengshou Gu and Ball, {Andrew D.}",
note = "Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: The authors would like to acknowledge the support from the China Scholarship Council (Grant No. 201608060042), the Natural Science Foundation of Guangdong Province (Grant No. 2017A030313291) and Innovating major training projects of Beijing Institute of Technology, Zhuhai (Grant No. XKCQ-2019-01). The authors would also appreciate the support from Dr Khalid Rabeyee in CEPE at University of Huddersfield. Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: The authors would like to acknowledge the support from the China Scholarship Council (Grant No. 201608060042), the Natural Science Foundation of Guangdong Province (Grant No. 2017A030313291) and Innovating major training projects of Beijing Institute of Technology, Zhuhai (Grant No. XKCQ-2019-01). The authors would also appreciate the support from Dr Khalid Rabeyee in CEPE at University of Huddersfield. Publisher Copyright: {\textcopyright} The Author(s) 2020. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2021",
month = may,
day = "1",
doi = "10.1177/1475921720949827",
language = "English",
volume = "20",
pages = "1231--1246",
journal = "Structural Health Monitoring",
issn = "1475-9217",
publisher = "SAGE Publications Ltd",
number = "3",
}