Generalized Vold-Kalman Filtering for Nonstationary Compound Faults Feature Extraction of Bearing and Gear

Dezun Zhao; Weidong Cheng; Robert X. Gao; Ruqiang Yan; Peng Wang

doi:10.1109/TIM.2019.2903700

Generalized Vold-Kalman Filtering for Nonstationary Compound Faults Feature Extraction of Bearing and Gear

Dezun Zhao, Weidong Cheng, Robert X. Gao, Ruqiang Yan, Peng Wang

Research output: Contribution to journal › Article › peer-review

40 Scopus citations

Abstract

Effective detection of multifaults in bearings and gears is a challenging issue in rotary machinery health monitoring. As such, a generalized Vold-Kalman filtering (GVKF)-based compound faults diagnosis method is presented in this paper. The technique includes four main steps: 1) a time-frequency ridge is separated from the time-frequency representation (TFR) of the vibration signal using a peak search method; 2) according to the time-frequency ridge, GVKF parameters corresponding to all the fault characteristic frequencies (FCFs) are estimated; 3) the fault feature components are obtained using the generalized demodulation transform (GDT) and the VKF with the GVKF parameters; and 4) the spectra obtained by the fast Fourier transform (FFT) are used to fault detection. The main contributions of the proposed method are as follows: 1) the influence of speed fluctuations and the unrelated harmonic components are removed through the integration of the GDT and the VKF and 2) the tachometerless GVKF parameters are defined and calculated to quantitatively detect different fault types, which avoids missed diagnosis and misdiagnosis. The proposed multifault diagnosis algorithm is verified by both simulation and experiment data. Comparison with other commonly used techniques has shown the advantage of the new method.

Original language	English
Article number	8678678
Pages (from-to)	401-410
Number of pages	10
Journal	IEEE Transactions on Instrumentation and Measurement
Volume	69
Issue number	2
DOIs	https://doi.org/10.1109/TIM.2019.2903700
State	Published - Feb 2020

Bibliographical note

Funding Information:
Manuscript received July 31, 2018; revised January 31, 2019; accepted February 22, 2019. Date of publication April 1, 2019; date of current version January 6, 2020. This work was supported by the National Natural Science Foundation of China under Grant 51335006 and Grant 51605244. The Associate Editor coordinating the review process was John Sheppard. (Corresponding author: Dezun Zhao.) D. Zhao is with the Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China (e-mail: dzzhao0903@gmail.com).

Funding Information:
Dr. Yan is a member of ASME. He was a recipient of the New Century Excellent Talents in University Award from the Ministry of Education in China, in 2009. He is an Associate Editor-in-Chief of the IEEE TRANS-ACTIONS ON INSTRUMENTATION AND MEASUREMENT.

Publisher Copyright:
© 1963-2012 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

Bearing
compound faults diagnosis
gear
generalized Vold-Kalman filtering (GVKF)
nonstationary

ASJC Scopus subject areas

Instrumentation
Electrical and Electronic Engineering

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10.1109/TIM.2019.2903700

Cite this

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title = "Generalized Vold-Kalman Filtering for Nonstationary Compound Faults Feature Extraction of Bearing and Gear",

abstract = "Effective detection of multifaults in bearings and gears is a challenging issue in rotary machinery health monitoring. As such, a generalized Vold-Kalman filtering (GVKF)-based compound faults diagnosis method is presented in this paper. The technique includes four main steps: 1) a time-frequency ridge is separated from the time-frequency representation (TFR) of the vibration signal using a peak search method; 2) according to the time-frequency ridge, GVKF parameters corresponding to all the fault characteristic frequencies (FCFs) are estimated; 3) the fault feature components are obtained using the generalized demodulation transform (GDT) and the VKF with the GVKF parameters; and 4) the spectra obtained by the fast Fourier transform (FFT) are used to fault detection. The main contributions of the proposed method are as follows: 1) the influence of speed fluctuations and the unrelated harmonic components are removed through the integration of the GDT and the VKF and 2) the tachometerless GVKF parameters are defined and calculated to quantitatively detect different fault types, which avoids missed diagnosis and misdiagnosis. The proposed multifault diagnosis algorithm is verified by both simulation and experiment data. Comparison with other commonly used techniques has shown the advantage of the new method.",

keywords = "Bearing, compound faults diagnosis, gear, generalized Vold-Kalman filtering (GVKF), nonstationary",

author = "Dezun Zhao and Weidong Cheng and Gao, {Robert X.} and Ruqiang Yan and Peng Wang",

note = "Funding Information: Manuscript received July 31, 2018; revised January 31, 2019; accepted February 22, 2019. Date of publication April 1, 2019; date of current version January 6, 2020. This work was supported by the National Natural Science Foundation of China under Grant 51335006 and Grant 51605244. The Associate Editor coordinating the review process was John Sheppard. (Corresponding author: Dezun Zhao.) D. Zhao is with the Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China (e-mail: dzzhao0903@gmail.com). Funding Information: Dr. Yan is a member of ASME. He was a recipient of the New Century Excellent Talents in University Award from the Ministry of Education in China, in 2009. He is an Associate Editor-in-Chief of the IEEE TRANS-ACTIONS ON INSTRUMENTATION AND MEASUREMENT. Publisher Copyright: {\textcopyright} 1963-2012 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",

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AU - Gao, Robert X.

AU - Yan, Ruqiang

AU - Wang, Peng

N1 - Funding Information: Manuscript received July 31, 2018; revised January 31, 2019; accepted February 22, 2019. Date of publication April 1, 2019; date of current version January 6, 2020. This work was supported by the National Natural Science Foundation of China under Grant 51335006 and Grant 51605244. The Associate Editor coordinating the review process was John Sheppard. (Corresponding author: Dezun Zhao.) D. Zhao is with the Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China (e-mail: dzzhao0903@gmail.com). Funding Information: Dr. Yan is a member of ASME. He was a recipient of the New Century Excellent Talents in University Award from the Ministry of Education in China, in 2009. He is an Associate Editor-in-Chief of the IEEE TRANS-ACTIONS ON INSTRUMENTATION AND MEASUREMENT. Publisher Copyright: © 1963-2012 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

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N2 - Effective detection of multifaults in bearings and gears is a challenging issue in rotary machinery health monitoring. As such, a generalized Vold-Kalman filtering (GVKF)-based compound faults diagnosis method is presented in this paper. The technique includes four main steps: 1) a time-frequency ridge is separated from the time-frequency representation (TFR) of the vibration signal using a peak search method; 2) according to the time-frequency ridge, GVKF parameters corresponding to all the fault characteristic frequencies (FCFs) are estimated; 3) the fault feature components are obtained using the generalized demodulation transform (GDT) and the VKF with the GVKF parameters; and 4) the spectra obtained by the fast Fourier transform (FFT) are used to fault detection. The main contributions of the proposed method are as follows: 1) the influence of speed fluctuations and the unrelated harmonic components are removed through the integration of the GDT and the VKF and 2) the tachometerless GVKF parameters are defined and calculated to quantitatively detect different fault types, which avoids missed diagnosis and misdiagnosis. The proposed multifault diagnosis algorithm is verified by both simulation and experiment data. Comparison with other commonly used techniques has shown the advantage of the new method.

AB - Effective detection of multifaults in bearings and gears is a challenging issue in rotary machinery health monitoring. As such, a generalized Vold-Kalman filtering (GVKF)-based compound faults diagnosis method is presented in this paper. The technique includes four main steps: 1) a time-frequency ridge is separated from the time-frequency representation (TFR) of the vibration signal using a peak search method; 2) according to the time-frequency ridge, GVKF parameters corresponding to all the fault characteristic frequencies (FCFs) are estimated; 3) the fault feature components are obtained using the generalized demodulation transform (GDT) and the VKF with the GVKF parameters; and 4) the spectra obtained by the fast Fourier transform (FFT) are used to fault detection. The main contributions of the proposed method are as follows: 1) the influence of speed fluctuations and the unrelated harmonic components are removed through the integration of the GDT and the VKF and 2) the tachometerless GVKF parameters are defined and calculated to quantitatively detect different fault types, which avoids missed diagnosis and misdiagnosis. The proposed multifault diagnosis algorithm is verified by both simulation and experiment data. Comparison with other commonly used techniques has shown the advantage of the new method.

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Generalized Vold-Kalman Filtering for Nonstationary Compound Faults Feature Extraction of Bearing and Gear

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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