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

• Published in: IEEE transactions on instrumentation and measurement Vol. 71; pp. 1 - 12
• Main Authors: Wang, Huan, Men, Tianli, Li, Yan-Fu
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Convolutional neural network (CNN); Correlation; Deep learning; Derailments; Feature extraction; High speed rail; high-speed train (HST); Machine learning; Multiplexing; Predictive models; Railroad accidents & safety; Railroad transportation; Railroad wheels; Representation learning; Tool wear; transformer; Transformers; Vibration; vibration signals; Vibrations; wheel wear prediction; Wheels

The wheel wear status of high-speed trains (HSTs) is an essential indicator for their safety and reliability. When the wheel wear exceeds the warning value without timely maintenance, it will seriously affect the dynamic performance of the HST and even cause a derailment accident. With HSTs and sens...