Multi-level health degree analysis of vehicle transmission system based on PSO- Indexed by: BP neural network data fusion

Abstract

In order to realize the evaluation of the vehicle transmission system health degree, a prediction model by multi-level data fusion method is established in this paper. The prediction model applies PSO(Particle Swarm Optimization)-BP(Back Propagation) neural network algorithm, calculates the whole machine health degree and each module respective weights from the test data. On this basis, it analyzes the error between the model calculated health degree and theoretical health degree. Then the research verifies the validity and prediction model accuracy. The health degree which is obtained by the single module feature parameters fusion, and the vehicle transmission system health degree is investigated, which is less effective compared to the three-level fusions. After that, by analyzing the vehicle transmission system multi-parameter feature weights, it is found that the mechanical module accounted for the largest damage rate, and the three modules influenced the vehicle transmission system health degree in the order of mechanical module, hydraulic module, and electric control module. The study has played a guiding role in the health management of complex equipment.

ADAS Simulation Result Dataset Processing Based on Improved BP Neural Network

“…According to the results of the above regression evaluation indicators, we can find that after optimizing the original BP neural network using the genetic algorithm, this paper has better generalization ability and prediction ability and can better predict the distance from the target vehicle after the AEB function is triggered and completely stopped. The author implemented the aforementioned enhanced algorithms (APSO-BP [30], PSO-BP [31,32], and SSA-BP [33,34]) and applied the same regression evaluation metrics to compare them with the GA-BP neural network. The results of this comparison are as follows (Table 4): The regression evaluation indicators demonstrate that all three improved BP neural networks have improved in accuracy compared to the original neural network.…”