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
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2022.3154827

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 sensor technology development, massive operation data can be obtained, which provides a new possibility for developing a data-driven algorithm for wheel wear prediction. To this end, this article proposes a novel transformer-based framework with multiplex local-global temporal fusion (LGF-Trans), which can be used for wheel wear prediction via vibration signals. First, a multiplex local temporal fusion architecture is proposed, composed of multiple local temporal attention networks (LTA-Networks). It can encode the local temporal correlation of the signal and improve the detail perception ability of the model. Subsequently, the transformer architecture is introduced, which uses the multi-head attention mechanism to fully encode the global temporal correlation features of vibration signals, thereby modeling the internal relationship between the input signal and the wheel wear status. LGF-Trans fully integrates the advantages of convolutional network and transformer architecture in local feature learning and global feature learning, thereby effectively extracting valuable features from massive noisy operating data. Experiments on the real operation dataset of CRH1A HST show that LGF-Trans can accurately predict wheel wear curves, and it has a better performance than the state-of-the-art deep learning methods. This confirms that LGF-Trans is expected to be a powerful tool for wheel wear prediction.