Multioperator Morphological Undecimated Wavelet for Wheelset Bearing Compound Fault Detection

Li, Yifan; Feng, Ke; Chen, Yuejian; Chen, Zaigang

doi:10.1109/tim.2023.3284937

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…As an important guiding, moving, and loading device of a train, the train wheelset tread will inevitably suffer from different types of defects, such as wear, cracks, and scratches, due to the long-term service of rolling contact [1][2][3]. If such defects are allowed to continue to worsen, not only will additional shock vibrations affecting ride comfort be caused, but serious defects, such as wheelset breakage, in extreme cases, can also eventuate.…”

Section: Introductionmentioning

confidence: 99%

Detection of Train Wheelset Tread Defects with Small Samples Based on Local Inference Constraint Network

Liu,

Jiang,

Wang

et al. 2024

Electronics

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Due to the long-term service through wheel-rail rolling contact, the train wheelset tread will inevitably suffer from different types of defects, such as wear, cracks, and scratches. The effective detection of wheelset tread defects can provide critical support for the operation and maintenance of trains. In this paper, a new method based on a local inference constraint network is proposed to detect wheelset tread defects, and the main purpose is to address the issue of insufficient feature spaces caused by small samples. First, a generative adversarial network is applied to generate diverse samples with semantic consistency. An attention mechanism module is introduced into the feature extraction network to increase the importance of defect features. Then, the residual spine network for local input decisions is constructed to establish an association between sample features and defect types. Furthermore, the network’s activation function is improved to obtain higher learning speed and accuracy with fewer parameters. Finally, the validity and feasibility of the proposed method are verified using experimental data.

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Section: Introductionmentioning

confidence: 99%

Detection of Train Wheelset Tread Defects with Small Samples Based on Local Inference Constraint Network

Liu,

Jiang,

Wang

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

A novel method based on the SCNGO‐ICEEMDAN and MCNN‐BiLSTM model for fault diagnosis of motor bearings for more electric aircraft

Yuan,

Liu,

Yin

et al. 2024

IET Electric Power Appl

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The fault signal characteristics of motor rolling bearings for more electric aircraft are easily masked by strong background noise. Directly using machine learning, deep learning, or other methods results in a lower accuracy in fault recognition. In this article, a Northern Goshawk algorithm using a fusion subtraction optimiser and Cauchy strategy (SCNGO) is proposed to optimise the number of white noise additions and amplitude weights in the improved full set empirical mode decomposition method based on adaptive noise (ICEEMDAN). Then, a multi‐scale convolutional neural network (MCNN) is used to extract the time–frequency domain features of the de‐noised signal and perform information fusion. Finally, the bidirectional long short‐term memory network (BiLSTM) was used to learn the faults' fusion features and complete the faults' recognition at different speeds. The research results show that the SCNGO‐ICEEMDAN and MCNN‐BiLSTMT model shows significant advantages in bearing fault recognition with an average recognition accuracy of 98.67% at various speeds.

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Fault diagnosis of motor bearing based on the SCNGO-ICEEMDAN method

Yuan,

Liu,

Yin

et al. 2024

2024 IEEE 7th International Electrical and Energy Conference (CIEEC)

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Multioperator Morphological Undecimated Wavelet for Wheelset Bearing Compound Fault Detection

Cited by 3 publications

References 33 publications

Detection of Train Wheelset Tread Defects with Small Samples Based on Local Inference Constraint Network

Detection of Train Wheelset Tread Defects with Small Samples Based on Local Inference Constraint Network

A novel method based on the SCNGO‐ICEEMDAN and MCNN‐BiLSTM model for fault diagnosis of motor bearings for more electric aircraft

Fault diagnosis of motor bearing based on the SCNGO-ICEEMDAN method

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