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

Dezuo Zhao, Weidong Cheng, Robert Gao, Ruqiang Yan, Peng Wang

Research output: Contribution to journalArticle

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 languageEnglish
Number of pages7
JournalIEEE Transactions on Instrumentation and Measurement
Early online date1 Apr 2019
DOIs
Publication statusE-pub ahead of print - 1 Apr 2019
Externally publishedYes

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Bearings (structural)
Demodulation
pattern recognition
Gears
Feature extraction
Rotating machinery
demodulation
Fault detection
Fast Fourier transforms
Failure analysis
ridges
Health
fault detection
Monitoring
machinery
health
Experiments
harmonics
vibration
simulation

Cite this

@article{e21b57563bbc4046929df5875f2d9ae3,
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.",
author = "Dezuo Zhao and Weidong Cheng and Robert Gao and Ruqiang Yan and Peng Wang",
year = "2019",
month = "4",
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doi = "10.1109/TIM.2019.2903700",
language = "English",
journal = "IEEE Transactions on Instrumentation and Measurement",
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Generalized Vold-Kalman Filtering for Nonstationary Compound Faults Feature Extraction of Bearing and Gear. / Zhao, Dezuo; Cheng, Weidong ; Gao, Robert; Yan, Ruqiang ; Wang, Peng.

In: IEEE Transactions on Instrumentation and Measurement, 01.04.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Generalized Vold-Kalman Filtering for Nonstationary Compound Faults Feature Extraction of Bearing and Gear

AU - Zhao, Dezuo

AU - Cheng, Weidong

AU - Gao, Robert

AU - Yan, Ruqiang

AU - Wang, Peng

PY - 2019/4/1

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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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