Vol. 3 No. 1 (2023): Issue 3
Articles

Analysis of Vehicle Fault Diagnosis Model Based on Causal Sequence-to-Sequence in Embedded Systems

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  • Weidong Huang,
  • Jiahuai Ma
Weidong Huang
GAC FIAT CHRYSLER Automobiles Co., Ltd., Changsha 410100, China； HYCAN Automobile Technology Co., Ltd., Guangzhou 510000, China
Jiahuai Ma
University of Florida, Herbert Wertheim College, FL, 32608, USA

Published 2023-09-12

Keywords

  • Vehicle fault diagnosis,
  • Deep learning,
  • Sequence-to-sequence,
  • Attention mechanism,
  • Causal learning,
  • Embedded systems
    • ...More
    Less

How to Cite

Huang, W., & Ma, J. (2023). Analysis of Vehicle Fault Diagnosis Model Based on Causal Sequence-to-Sequence in Embedded Systems. Optimizations in Applied Machine Learning, 3(1). Retrieved from https://ojs.mri-pub.com/index.php/OAML/article/view/74
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

The rapid development of the automotive industry has intensified the challenges faced by traditional fault diagnosis systems. This study proposes an efficient vehicle fault diagnosis model based on deep learning to improve fault identification accuracy and real-time performance, facilitating deployment in embedded systems. The model integrates a sequence-to-sequence architecture, an attention mechanism, and causal learning. The sequence-to-sequence structure captures complex time-series dependencies, while the attention mechanism enhances focus on critical features, improving fault pattern recognition. Causal learning further strengthens the model's understanding of fault relationships, enhancing diagnostic performance. Experimental evaluation on real-world vehicle datasets, including sensor data and maintenance records, demonstrates the model's superiority over state-of-the-art methods in accuracy, precision, recall, and F1 score. The results validate the model’s effectiveness in complex fault scenarios and its potential for embedded system integration. This research provides a robust foundation for advancing real-time data analysis in in-vehicle diagnostic systems within the Internet of Things framework.

 

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