Design of embedded identification system for motor vehicle engine fault based on deep learning

Xi Chen; Yanxing Cheng; Jinsong Yang; Jinquan Zhang; Yizhen Liao

doi:10.1117/12.3006133

10 October 2023 Design of embedded identification system for motor vehicle engine fault based on deep learning

Xi Chen, Yanxing Cheng, Jinsong Yang, Jinquan Zhang, Yizhen Liao

Author Affiliations +

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127991O (2023) https://doi.org/10.1117/12.3006133
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Aiming at the low efficiency of fault identification in the process of engine fault identification, an embedded identification system of motor vehicle engine fault based on deep learning is designed. A hardware system consisting of sensor, core processor and communicator is established. The sensor module contains qualitative sensor and quantitative sensor. The qualitative sensor adopts LEI sensor, the quantitative sensor adopts TDK sensor, and the core processor is used as the central processor. The insulation fault information of three bands of high, medium and low frequency is decomposed, the feature vector is determined, and the fault information extraction program is established to complete the design of embedded identification system for motor vehicle engine fault. The experimental results show that the designed system has the best denoising effect, retains the overall trend of the original signal to a greater extent, and presents the detailed information in the signal well, which improves the fault identification accuracy and has stronger fault identification performance.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xi Chen, Yanxing Cheng, Jinsong Yang, Jinquan Zhang, and Yizhen Liao "Design of embedded identification system for motor vehicle engine fault based on deep learning", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127991O (10 October 2023); https://doi.org/10.1117/12.3006133

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KEYWORDS

Signal processing

Sensors

Embedded systems

System identification

Deep learning

Telecommunications

Data transmission

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