Condition monitoring of train transmission systems based on multimodal fusion improved transformer network DOI Open Access

Cun Shi,

Shengyuan Zhao,

Xiying Chen

et al.

Sound&Vibration, Journal Year: 2025, Volume and Issue: 59(2), P. 2904 - 2904

Published: April 25, 2025

The train transmission system is a critical component of railway operations, playing pivotal role in ensuring service safety and reliability. However, existing condition monitoring approaches face two major challenges: (1) the coupling rich multimodal signals, such as vibration, acoustics, current, rotational speed, often overlooked, limiting accuracy; (2) small data problem signals adversely affects performance neural networks. To address these issues, this paper proposes Multimodal Fusion Improved Transformer Network for Condition Monitoring Train Transmission Systems. proposed network first explores interdependencies among different modalities compresses to reduced dimensions through correlation analysis. It then infers global dependencies computing self-attention scores based on Q, K, V matrices. approach better than traditional CNN-based models handling single-modality constraints, with former demonstrated be more accurate trustworthy publicly available datasets.

Language: Английский

Condition monitoring of train transmission systems based on multimodal fusion improved transformer network DOI Open Access

Cun Shi,

Shengyuan Zhao,

Xiying Chen

et al.

Sound&Vibration, Journal Year: 2025, Volume and Issue: 59(2), P. 2904 - 2904

Published: April 25, 2025

The train transmission system is a critical component of railway operations, playing pivotal role in ensuring service safety and reliability. However, existing condition monitoring approaches face two major challenges: (1) the coupling rich multimodal signals, such as vibration, acoustics, current, rotational speed, often overlooked, limiting accuracy; (2) small data problem signals adversely affects performance neural networks. To address these issues, this paper proposes Multimodal Fusion Improved Transformer Network for Condition Monitoring Train Transmission Systems. proposed network first explores interdependencies among different modalities compresses to reduced dimensions through correlation analysis. It then infers global dependencies computing self-attention scores based on Q, K, V matrices. approach better than traditional CNN-based models handling single-modality constraints, with former demonstrated be more accurate trustworthy publicly available datasets.

Language: Английский

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