Fault Detection of Rolling Bearings Using an Ensemble Average Autocorrelation Based Stochastic Subspace Identification

Yuandong Xu, Haiyang Li, Dong Zhen, Ibrahim.A.M Rehab, Fengshou Gu, Andrew Ball

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Rolling bearings are the crucial parts of rotating machines. The detection and diagnoses of their defects at early stages are significant for ensuring safety and efficient operations. Usually, the vibration feature associated with bearing faults are submerged by the heavy background noise and nonstationary impacts. To enhance detection performance, this pa-per proposes a novel method developed based on ensemble average autocorrelation and stochastic subspace identification (SSI) techniques. It establishes the theoretical basis of the method based on the general characteristics of bearing vibration signals under faults. Then it examines the robustness of techniques under different level noise, which leads to an optimal selection of centre frequencies that have high signal to noise ratio and thereby high accuracy of envelope analysis for fault diagnosis. Both simulation and experimental results show that the proposed method is able to extract bearing fault signatures at very low signal to noise ratio (<-20dB) and consequently produces accurate detection.
Original languageEnglish
Title of host publication24th International Congress on Sound and Vibration 2017 (ICSV 24)
PublisherCurran Associates, Inc
Pages2607-2614
Number of pages8
Volume4
ISBN (Print)9781510845855
Publication statusPublished - Sep 2017
Event24th International Congress on Sound and Vibration - Park Plaza Westminster Bridge Hotel, London, United Kingdom
Duration: 23 Jul 201727 Jul 2017
Conference number: 24
http://www.icsv24.org/ (Link to Congress Website)

Conference

Conference24th International Congress on Sound and Vibration
Abbreviated titleICSV24
CountryUnited Kingdom
CityLondon
Period23/07/1727/07/17
Internet address

Fingerprint

Bearings (structural)
Fault detection
Autocorrelation
Signal to noise ratio
Failure analysis
Defects

Cite this

Xu, Y., Li, H., Zhen, D., Rehab, I. A. M., Gu, F., & Ball, A. (2017). Fault Detection of Rolling Bearings Using an Ensemble Average Autocorrelation Based Stochastic Subspace Identification. In 24th International Congress on Sound and Vibration 2017 (ICSV 24) (Vol. 4, pp. 2607-2614). Curran Associates, Inc.
Xu, Yuandong ; Li, Haiyang ; Zhen, Dong ; Rehab, Ibrahim.A.M ; Gu, Fengshou ; Ball, Andrew. / Fault Detection of Rolling Bearings Using an Ensemble Average Autocorrelation Based Stochastic Subspace Identification. 24th International Congress on Sound and Vibration 2017 (ICSV 24). Vol. 4 Curran Associates, Inc, 2017. pp. 2607-2614
@inproceedings{6b4d1dc775ba4993b1daafe1e693106f,
title = "Fault Detection of Rolling Bearings Using an Ensemble Average Autocorrelation Based Stochastic Subspace Identification",
abstract = "Rolling bearings are the crucial parts of rotating machines. The detection and diagnoses of their defects at early stages are significant for ensuring safety and efficient operations. Usually, the vibration feature associated with bearing faults are submerged by the heavy background noise and nonstationary impacts. To enhance detection performance, this pa-per proposes a novel method developed based on ensemble average autocorrelation and stochastic subspace identification (SSI) techniques. It establishes the theoretical basis of the method based on the general characteristics of bearing vibration signals under faults. Then it examines the robustness of techniques under different level noise, which leads to an optimal selection of centre frequencies that have high signal to noise ratio and thereby high accuracy of envelope analysis for fault diagnosis. Both simulation and experimental results show that the proposed method is able to extract bearing fault signatures at very low signal to noise ratio (<-20dB) and consequently produces accurate detection.",
keywords = "Fault detection, Bearings, SSI, Autocorrelation, Envelope",
author = "Yuandong Xu and Haiyang Li and Dong Zhen and Ibrahim.A.M Rehab and Fengshou Gu and Andrew Ball",
year = "2017",
month = "9",
language = "English",
isbn = "9781510845855",
volume = "4",
pages = "2607--2614",
booktitle = "24th International Congress on Sound and Vibration 2017 (ICSV 24)",
publisher = "Curran Associates, Inc",

}

Xu, Y, Li, H, Zhen, D, Rehab, IAM, Gu, F & Ball, A 2017, Fault Detection of Rolling Bearings Using an Ensemble Average Autocorrelation Based Stochastic Subspace Identification. in 24th International Congress on Sound and Vibration 2017 (ICSV 24). vol. 4, Curran Associates, Inc, pp. 2607-2614, 24th International Congress on Sound and Vibration, London, United Kingdom, 23/07/17.

Fault Detection of Rolling Bearings Using an Ensemble Average Autocorrelation Based Stochastic Subspace Identification. / Xu, Yuandong; Li, Haiyang; Zhen, Dong; Rehab, Ibrahim.A.M; Gu, Fengshou; Ball, Andrew.

24th International Congress on Sound and Vibration 2017 (ICSV 24). Vol. 4 Curran Associates, Inc, 2017. p. 2607-2614.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Fault Detection of Rolling Bearings Using an Ensemble Average Autocorrelation Based Stochastic Subspace Identification

AU - Xu, Yuandong

AU - Li, Haiyang

AU - Zhen, Dong

AU - Rehab, Ibrahim.A.M

AU - Gu, Fengshou

AU - Ball, Andrew

PY - 2017/9

Y1 - 2017/9

N2 - Rolling bearings are the crucial parts of rotating machines. The detection and diagnoses of their defects at early stages are significant for ensuring safety and efficient operations. Usually, the vibration feature associated with bearing faults are submerged by the heavy background noise and nonstationary impacts. To enhance detection performance, this pa-per proposes a novel method developed based on ensemble average autocorrelation and stochastic subspace identification (SSI) techniques. It establishes the theoretical basis of the method based on the general characteristics of bearing vibration signals under faults. Then it examines the robustness of techniques under different level noise, which leads to an optimal selection of centre frequencies that have high signal to noise ratio and thereby high accuracy of envelope analysis for fault diagnosis. Both simulation and experimental results show that the proposed method is able to extract bearing fault signatures at very low signal to noise ratio (<-20dB) and consequently produces accurate detection.

AB - Rolling bearings are the crucial parts of rotating machines. The detection and diagnoses of their defects at early stages are significant for ensuring safety and efficient operations. Usually, the vibration feature associated with bearing faults are submerged by the heavy background noise and nonstationary impacts. To enhance detection performance, this pa-per proposes a novel method developed based on ensemble average autocorrelation and stochastic subspace identification (SSI) techniques. It establishes the theoretical basis of the method based on the general characteristics of bearing vibration signals under faults. Then it examines the robustness of techniques under different level noise, which leads to an optimal selection of centre frequencies that have high signal to noise ratio and thereby high accuracy of envelope analysis for fault diagnosis. Both simulation and experimental results show that the proposed method is able to extract bearing fault signatures at very low signal to noise ratio (<-20dB) and consequently produces accurate detection.

KW - Fault detection

KW - Bearings

KW - SSI

KW - Autocorrelation

KW - Envelope

UR - http://www.proceedings.com/35564.html

M3 - Conference contribution

SN - 9781510845855

VL - 4

SP - 2607

EP - 2614

BT - 24th International Congress on Sound and Vibration 2017 (ICSV 24)

PB - Curran Associates, Inc

ER -

Xu Y, Li H, Zhen D, Rehab IAM, Gu F, Ball A. Fault Detection of Rolling Bearings Using an Ensemble Average Autocorrelation Based Stochastic Subspace Identification. In 24th International Congress on Sound and Vibration 2017 (ICSV 24). Vol. 4. Curran Associates, Inc. 2017. p. 2607-2614