Detecting Bearing Faults Using an Ensemble Average Autocorrelation Based Stochastic Subspace Identification

Yuandong Xu, Pieter van Vuuren, Xiaoli Tang, Fengshou Gu, Andrew Ball

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

Abstract

Envelope analysis plays an important role in the field of bearing faults detection. Since the development of this technique, the determination of optimal bands has been a prior challenge. Fast Kurtogram (FK) is an outstanding approach to select an optimal band for further analysis; however, fast Kurtogram is not robust enough to withstand the influence of white noise and large aperiodic impulses. Hence, a more robust method is introduced to extract the narrow bands for envelope analysis, which is ensemble average autocorrelation based stochastic subspace identification (SSI). The detector performs well in denoising and highlighting the periodic impulses owing to the outstanding characteristics of autocorrelation function and stochastic subspace identification. Considering the results of simulation study and experimental evaluation, it can be concluded that the proposed method is more effective and robust to detect bearing faults than fast Kurtogram.
LanguageEnglish
Title of host publicationProceedings of COMADEM 2017
EditorsI. Sherrington, A. Onsy, R. Rao, H. Brooks, J. Philip
Pages389-395
Number of pages7
Publication statusPublished - Jul 2017
Event30th International Congress & Exhibition on Condition Monitoring and Diagnostic Engineering Management - University of Central Lancashire, Preston, United Kingdom
Duration: 10 Jul 201713 Jul 2017
Conference number: 30
http://www.comadem2017.com/ (Link to Conference Website)

Conference

Conference30th International Congress & Exhibition on Condition Monitoring and Diagnostic Engineering Management
Abbreviated titleCOMADEM 2017
CountryUnited Kingdom
CityPreston
Period10/07/1713/07/17
Internet address

Fingerprint

Bearings (structural)
Autocorrelation
White noise
Fault detection
Detectors

Cite this

Xu, Y., van Vuuren, P., Tang, X., Gu, F., & Ball, A. (2017). Detecting Bearing Faults Using an Ensemble Average Autocorrelation Based Stochastic Subspace Identification. In I. Sherrington, A. Onsy, R. Rao, H. Brooks, & J. Philip (Eds.), Proceedings of COMADEM 2017 (pp. 389-395)
Xu, Yuandong ; van Vuuren, Pieter ; Tang, Xiaoli ; Gu, Fengshou ; Ball, Andrew. / Detecting Bearing Faults Using an Ensemble Average Autocorrelation Based Stochastic Subspace Identification. Proceedings of COMADEM 2017. editor / I. Sherrington ; A. Onsy ; R. Rao ; H. Brooks ; J. Philip. 2017. pp. 389-395
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title = "Detecting Bearing Faults Using an Ensemble Average Autocorrelation Based Stochastic Subspace Identification",
abstract = "Envelope analysis plays an important role in the field of bearing faults detection. Since the development of this technique, the determination of optimal bands has been a prior challenge. Fast Kurtogram (FK) is an outstanding approach to select an optimal band for further analysis; however, fast Kurtogram is not robust enough to withstand the influence of white noise and large aperiodic impulses. Hence, a more robust method is introduced to extract the narrow bands for envelope analysis, which is ensemble average autocorrelation based stochastic subspace identification (SSI). The detector performs well in denoising and highlighting the periodic impulses owing to the outstanding characteristics of autocorrelation function and stochastic subspace identification. Considering the results of simulation study and experimental evaluation, it can be concluded that the proposed method is more effective and robust to detect bearing faults than fast Kurtogram.",
keywords = "Fault detection, Ensemble average autocorrelation, SSI",
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Xu, Y, van Vuuren, P, Tang, X, Gu, F & Ball, A 2017, Detecting Bearing Faults Using an Ensemble Average Autocorrelation Based Stochastic Subspace Identification. in I Sherrington, A Onsy, R Rao, H Brooks & J Philip (eds), Proceedings of COMADEM 2017. pp. 389-395, 30th International Congress & Exhibition on Condition Monitoring and Diagnostic Engineering Management, Preston, United Kingdom, 10/07/17.

Detecting Bearing Faults Using an Ensemble Average Autocorrelation Based Stochastic Subspace Identification. / Xu, Yuandong; van Vuuren, Pieter; Tang, Xiaoli; Gu, Fengshou; Ball, Andrew.

Proceedings of COMADEM 2017. ed. / I. Sherrington; A. Onsy; R. Rao; H. Brooks; J. Philip. 2017. p. 389-395.

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

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T1 - Detecting Bearing Faults Using an Ensemble Average Autocorrelation Based Stochastic Subspace Identification

AU - Xu, Yuandong

AU - van Vuuren, Pieter

AU - Tang, Xiaoli

AU - Gu, Fengshou

AU - Ball, Andrew

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N2 - Envelope analysis plays an important role in the field of bearing faults detection. Since the development of this technique, the determination of optimal bands has been a prior challenge. Fast Kurtogram (FK) is an outstanding approach to select an optimal band for further analysis; however, fast Kurtogram is not robust enough to withstand the influence of white noise and large aperiodic impulses. Hence, a more robust method is introduced to extract the narrow bands for envelope analysis, which is ensemble average autocorrelation based stochastic subspace identification (SSI). The detector performs well in denoising and highlighting the periodic impulses owing to the outstanding characteristics of autocorrelation function and stochastic subspace identification. Considering the results of simulation study and experimental evaluation, it can be concluded that the proposed method is more effective and robust to detect bearing faults than fast Kurtogram.

AB - Envelope analysis plays an important role in the field of bearing faults detection. Since the development of this technique, the determination of optimal bands has been a prior challenge. Fast Kurtogram (FK) is an outstanding approach to select an optimal band for further analysis; however, fast Kurtogram is not robust enough to withstand the influence of white noise and large aperiodic impulses. Hence, a more robust method is introduced to extract the narrow bands for envelope analysis, which is ensemble average autocorrelation based stochastic subspace identification (SSI). The detector performs well in denoising and highlighting the periodic impulses owing to the outstanding characteristics of autocorrelation function and stochastic subspace identification. Considering the results of simulation study and experimental evaluation, it can be concluded that the proposed method is more effective and robust to detect bearing faults than fast Kurtogram.

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Xu Y, van Vuuren P, Tang X, Gu F, Ball A. Detecting Bearing Faults Using an Ensemble Average Autocorrelation Based Stochastic Subspace Identification. In Sherrington I, Onsy A, Rao R, Brooks H, Philip J, editors, Proceedings of COMADEM 2017. 2017. p. 389-395