Multi-level health degree analysis of vehicle transmission system based on PSO-
BP neural network data fusion
More details
Hide details
Online publication date: 2023-01-27
Publication date: 2023-01-27
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(1):4
HIGHLIGHTS
- Accurate prediction vehicle transmission system health degree,
- Mechanical module has the greatest impact on the system health,
- Use PSO-BP neural network integrates 20 types characteristic indicators,
- Considered three modules influence on system health.
KEYWORDS
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.