An Adaptive Demodulation Band Segmentation Method to Optimize Spectral Boundary and Its Application for Wheelset-Bearing Fault Detection

Wheelset-bearing working states directly affect the stability of bogies, and existing defects may threaten the running safety of high-speed trains. Thus, the fault detection of wheelset-bearing is of great importance. In this paper, a novel wheelset-bearing fault detection method, named adaptive aut...

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Published inIEEE transactions on instrumentation and measurement Vol. 71; p. 1
Main Authors Zhang, Qingsong, Ding, Jianming, Zhao, Wentao
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Autocorrelated kurtogram
Autocorrelation
Bandwidth
Bandwidths
Demodulation
Fault detection
Faults
Filter banks
Frequency conversion
Frequency estimation
High speed rail
Information filters
Interference
Kurtosis
Low-pass filters
non-stationary signals
Resonant frequency
Rotation
Segmentation
Signal to noise ratio
spectral segmentation
Undercarriages
wheelset-bearing defects
Wheelsets
Online AccessGet full text
ISSN0018-9456
1557-9662
DOI10.1109/TIM.2022.3178484

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Abstract Wheelset-bearing working states directly affect the stability of bogies, and existing defects may threaten the running safety of high-speed trains. Thus, the fault detection of wheelset-bearing is of great importance. In this paper, a novel wheelset-bearing fault detection method, named adaptive autocorrelated kurtogram (AAK), is proposed based on the developed autocorrelated kurtosis and the presented spectral segmentation method. Autocorrelated kurtosis is designed to reduce the unrelated components interference and increase the signal-to-noise ratio (SNR). The spectral segmentation method is proposed to obtain the fault zone as completely as possible. Based on the feature that rotation frequency is related to bearing fault frequency, the rotation frequency-based window size selection and extension strategy is proposed. With different frequency levels, the AAK is formed. The proposed method is validated by simulated and experimental data. The results show that this method can automatically and adaptively search for a reasonable demodulation bandwidth according to the fault information feature and resonance region position. The method can estimate the center frequency and bandwidth and avoid unrelated component interference. The AAK can provide accurate detection results and possesses excellent performance. Thus, it is suitable for wheelset-bearing fault detection.
AbstractList Wheelset-bearing working states directly affect the stability of bogies, and existing defects may threaten the running safety of high-speed trains. Thus, the fault detection of wheelset bearing is of great importance. In this article, a novel wheelset-bearing fault detection method, named adaptive autocorrelated kurtogram (AAK), is proposed based on the developed autocorrelated kurtosis and the presented spectral segmentation method. Autocorrelated kurtosis is designed to reduce the unrelated component’s interference and increase the signal-to-noise ratio (SNR). The spectral segmentation method is proposed to obtain the fault zone as completely as possible. Based on the feature that rotation frequency is related to bearing fault frequency, the rotation frequency-based window size selection and extension strategy is proposed. With different frequency levels, the AAK is formed. The proposed method is validated by simulated and experimental data. The results show that this method can automatically and adaptively search for a reasonable demodulation bandwidth according to the fault information feature and resonance region position. The method can estimate the center frequency and bandwidth and avoid unrelated component interference. The AAK can provide accurate detection results and possesses excellent performance. Thus, it is suitable for wheelset-bearing fault detection.
Wheelset-bearing working states directly affect the stability of bogies, and existing defects may threaten the running safety of high-speed trains. Thus, the fault detection of wheelset-bearing is of great importance. In this paper, a novel wheelset-bearing fault detection method, named adaptive autocorrelated kurtogram (AAK), is proposed based on the developed autocorrelated kurtosis and the presented spectral segmentation method. Autocorrelated kurtosis is designed to reduce the unrelated components interference and increase the signal-to-noise ratio (SNR). The spectral segmentation method is proposed to obtain the fault zone as completely as possible. Based on the feature that rotation frequency is related to bearing fault frequency, the rotation frequency-based window size selection and extension strategy is proposed. With different frequency levels, the AAK is formed. The proposed method is validated by simulated and experimental data. The results show that this method can automatically and adaptively search for a reasonable demodulation bandwidth according to the fault information feature and resonance region position. The method can estimate the center frequency and bandwidth and avoid unrelated component interference. The AAK can provide accurate detection results and possesses excellent performance. Thus, it is suitable for wheelset-bearing fault detection.
Author Ding, Jianming
Zhao, Wentao
Zhang, Qingsong
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Snippet Wheelset-bearing working states directly affect the stability of bogies, and existing defects may threaten the running safety of high-speed trains. Thus, the...
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SubjectTerms Autocorrelated kurtogram
Autocorrelation
Bandwidth
Bandwidths
Demodulation
Fault detection
Faults
Filter banks
Frequency conversion
Frequency estimation
High speed rail
Information filters
Interference
Kurtosis
Low-pass filters
non-stationary signals
Resonant frequency
Rotation
Segmentation
Signal to noise ratio
spectral segmentation
Undercarriages
wheelset-bearing defects
Wheelsets
Title An Adaptive Demodulation Band Segmentation Method to Optimize Spectral Boundary and Its Application for Wheelset-Bearing Fault Detection
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