Transformer for High-Speed Train Wheel Wear Prediction with Multiplex Local-Global Temporal Fusion

Wang, Huan; Men, Tianli; Li, Yan‐Fu

doi:10.1109/qrs-c55045.2021.00181

Cited by 3 publications

(1 citation statement)

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“…In recent years, deep learning has attracted more and more attention in the field of fault diagnosis [8][9][10][11]. Compared with traditional methods, the deep learning method could extract features from lower level to higher level automatically based on multiple nonlinear operations, and thus it could diagnose with higher intelligence.…”

Section: Introductionmentioning

confidence: 99%

Long-range Dependencies Learning Based on Non-Local 1D-Convolutional Neural Network for Rolling Bearing Fault Diagnosis

Wang

Liu

2022

JDMD

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In the field of data-driven bearing fault diagnosis, convolutional neural network (CNN) has been widely researched and applied due to its superior feature extraction and classification ability. However, the convolutional operation could only process a local neighborhood at a time and thus lack ability of capturing long-range dependencies. Therefore, building an efficient learning method for long-range dependencies is crucial to comprehend and express signal features considering that the vibration signals obtained in a real industrial environment always have strong instability, periodicity, and temporal correlation. This paper introduces non-local mean to the CNN and presents 1D non-local block (1D-NLB) to extract long-range dependencies. The 1D-NLB computes the response at a position as a weighted average value of the features at all positions. Based on it, we propose a non-local 1D convolutional neural network (NL-1DCNN) aiming at rolling bearing fault diagnosis. Furthermore, the 1D-NLB could be simply plugged into most existing deep learning architecture to improve their fault diagnosis ability. Under multiple noise conditions, the 1D-NLB improves the performance of the CNN on the wheelset bearing dataset of high-speed train and the Case Western Reserve University bearing dataset. The experiment results show that the NL-1DCNN exhibits superior results compared with six state-of-the-art fault diagnosis methods.

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Section: Introductionmentioning

confidence: 99%