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

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.