Automobile Rear Axle Health Monitoring Technology Based on Deep Learning

Le résumé fourni par la source

The rear axle is prone to fatigue fracture due to the complicated load impact throughout the vehicle’s service procedure. Real-time suspension health monitoring during operation is an excellent strategy for avoiding such situations. This paper takes the automobile rear axle as the research object and uses the deep learning model to study the rear axle health monitoring technology. A damage prediction model combining multi-Gaussian fitting and long-term and short-term memory neural network is proposed for automobile rear axle health monitoring. The feature extraction method based on multi-Gaussian fitting can obtain comprehensive power spectral density information with fewer feature parameters, which is conducive to improve the calculation efficiency. The LSTM model can accurately identify the fatigue damage value of the rear axle from the extracted characteristic information and ensure the high precision of the damage prediction results. Several deep learning-based comparison models are used to demonstrate that the GAPSD LSTM model has good prediction accuracy and computation efficiency at the same time. It has been proven that the GAPSD LSTM model can efficiently and accurately forecast the fatigue damage of the rear axle by comparing the damage prediction results of all deep learning models.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.