Locomotive Gear Fault Diagnosis Based on Wavelet Bispectrum of Motor Current

Zhang, Mingming; Yang, Jiangtian; Zhang, Zhang

doi:10.1155/2021/5554777

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…The wavelet energy entropy can be used for a preliminary diagnosis of the signal, while the dual-tree complex wavelet transform can provide further insight into the fault type. Similarly, based on the current signal, Zhang et al [67] proposed a fault diagnosis method for locomotive gear based on wavelet bispectrum (WB) and wavelet bispectrum entropy. Since the motor current in a faulty gear system contains not only fault-related frequency information but also power supply frequency and gear meshing-related frequency, extracting the fault frequency from it is challenging.…”

Section: Signal Processing Algorithmsmentioning

confidence: 99%

“…Temperature signals can indicate the overall operation state of the gearbox but are hard to tell specific types of faults. The fault diagnosis method of railway vehicle gearbox based on motor stator current signal analysis does not need additional sensors, which is more cost-effective and convenient for continuous monitoring in railway vehicles [67].…”

Section: Signal Processing Algorithmsmentioning

confidence: 99%

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Dynamics and Fault Diagnosis of Railway Vehicle Gearboxes: A Review

Zhao,

Chen

2024

JDMD

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The railway vehicle gearbox is an important part of the railway vehicle traction transmission system which ensures the smooth running of railway vehicles. However, as the running speed of railway vehicles continues to increase, the railway vehicle gearbox is exposed to a more demanding operating environment. Under both internal and external excitations, the gearbox is prone to faults such as fatigue cracks, broken teeth, etc. It is crucial to detect these faults before they result in severe failures and accidents. Therefore, understanding the dynamics and fault diagnosis of railway vehicle gearbox is needed. At present, there is a lack of systematic review of railway vehicle gearbox dynamics and fault diagnosis. So, this paper systematically summarizes the research progress on railway vehicle gearbox dynamics and fault diagnosis. To this end, this paper first summarizes the latest research progress on the dynamics of railway vehicle gearboxes. The dynamics and vibration characteristics of the gearbox are summarized under internal and external excitations, as well as faulty conditions. Then, the state-of-the-art signal processing and artificial intelligence methods for fault diagnosis of railway vehicle gearboxes are reviewed. In the end, future research prospects are given. Conflict of Interest Statement The authors declare no conflicts of interest.

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Section: Signal Processing Algorithmsmentioning

confidence: 99%

Section: Signal Processing Algorithmsmentioning

confidence: 99%

Dynamics and Fault Diagnosis of Railway Vehicle Gearboxes: A Review

Zhao,

Chen

2024

JDMD

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Motor Current Time-Varying Quadratic Phase Coupling Analysis and Its Application in Traction Motor Fault Detection Under Varying-Speed Condition

Yang,

Li,

Zhang

et al. 2024

IEEE Sensors J.

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Repetitive Transient Extraction Using the Optimized SES Entropy Wavelet for Fault Diagnosis of Rotating Machinery

Wang

Zhu

et al. 2021

Shock and Vibration

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Repetitive transients are usually generated in the monitoring data when a fault occurs on the machinery. As a result, many methods such as kurtogram and optimized Morlet wavelet and kurtosis method are proposed to extract the repetitive transients for fault diagnosis. However, one shortcoming of these methods is that they are constructed based on the index of kurtosis and are sensitive to the impulsive noise, leading to failure in accurately diagnosing the fault of the machinery operating under harsh environment. To address this issue, an optimized SES entropy wavelet method is proposed. In the proposed method, the optimized parameters including bandwidth and central frequency of Morlet wavelets are selected. Then, based on the wavelet coefficients decomposed using the optimized Morlet wavelet, the SES entropy is calculated to select the scales of wavelet coefficients. Finally, the repetitive transients are reconstructed based on the denoising wavelet coefficients of the selected scales. One simulation case and vibration data collected from the experimental setup are used to verify the effectiveness of the proposed method. The simulated and experimental analyses showed that the signal-to-noise ratio (SNR) of the proposed method has the largest value. Specifically, the SNR in the experimental analysis of the proposed method is 0.6, while that of the other three methods is 0.043, 0.0065, and 0.0045, respectively. Therefore, the result shows that the proposed method is superior to the traditional methods for repetitive transient extraction from the vibration data suffered from impulsive noise.

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Locomotive Gear Fault Diagnosis Based on Wavelet Bispectrum of Motor Current

Cited by 3 publications

References 43 publications

Dynamics and Fault Diagnosis of Railway Vehicle Gearboxes: A Review

Dynamics and Fault Diagnosis of Railway Vehicle Gearboxes: A Review

Motor Current Time-Varying Quadratic Phase Coupling Analysis and Its Application in Traction Motor Fault Detection Under Varying-Speed Condition

Repetitive Transient Extraction Using the Optimized SES Entropy Wavelet for Fault Diagnosis of Rotating Machinery

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