Long and short term fault prediction using the VToMe-BiGRU algorithm for electric drive systems
Abstract With the rapid development of new energy vehicle technology, electric drive systems play a crucial role in the modern automotive industry. Ensuring the efficient and stable operation as well as reliability of electric drive systems has become a critical task. In order to prevent serious fau...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-07546-w |
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| Summary: | Abstract With the rapid development of new energy vehicle technology, electric drive systems play a crucial role in the modern automotive industry. Ensuring the efficient and stable operation as well as reliability of electric drive systems has become a critical task. In order to prevent serious faults in the short-term leading to potential accidents, this paper proposes an innovative approach for embedding the Token Merging (ToMe) algorithm into the Vision Transformer (ViT), called the VToMe algorithm and combining it with the Bidirectional Gated Recurrent Unit (BiGRU) network to form the VToMe-BiGRU architecture for electric drive system fault prediction. Specifically, the VToMe algorithm achieves stable detection of medium to long term system faults, while the BiGRU network achieves rapid fault prediction in the short term. The VToMe-BiGRU is an intelligent analysis method applied to automobile workshops, which is closer to the data source for data processing and analysis, alleviates the strong dependence on real-time network transmission, reduces the time consuming and labor-intensive process of manually extracting and analyzing the features, and improves the accuracy and reliability of the fault prediction. The optimized VToMe-BiGRU algorithm combines the Transformer model and the BiGRU network, which effectively captures the critical features in the electric drive system data, thus improving the fault prediction performance. Experimental validation on real-world electric vehicle (EV) maintenance datasets demonstrates outstanding performance of the proposed method. The multi-class fault classification achieves an average accuracy of 93.49% with a 32 $$\times$$ 32 patch size, outperforming state-of-the-art ViT++ by 0.12% while enhancing inference speed by 28% (32 FPS vs. 25 FPS for ViT++) to balance high precision and real-time efficiency. The short-term prediction yields a root-mean-square error (RMSE) as low as 6.33 and an accuracy (ACC) of 74.7% for complex fault modes such as bearing inner ring fault, surpassing traditional GRU/RNN models by over 20% in prediction accuracy. Moreover, the VToMe algorithm reduces computational complexity by 25% through hierarchical token merging, enabling efficient processing of high-dimensional sensor data without performance degradation. This research establishes a robust framework for real-time diagnosis of EV drive systems, effectively detecting critical faults like battery over-discharge and motor encoder errors with minimized false positives (FP < 5%), enhancing system reliability, reducing maintenance costs, and supporting proactive safety measures in EV applications. |
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| ISSN: | 2045-2322 |