基于改进SVM的车辆传动系统故障诊断方法

Fault diagnosis and performance evaluation with vehicle transmission system test data can play a role in fault warning, improving reliability, and further improving vehicle performance. However, the test data are very large and unbalanced, possess high dimensionality and noise, which make the traditional data analysis algorithm produce sub-optimal classification model. In order to solve the above problems, a new improved support vector machine (SVM) algorithm was proposed for imbalanced data classification. The algorithm was arranged to present different weights for each sample, improve the design of fuzzy membership degree with Mahalanobis distance to eliminate the interference of variable correlation, and to output the failure probability under normal state at the same time. The experimental results show that the algorithm can effectively improve the accuracy of fault diagnosis, and at the same time can use the probability output model to carry out fault warning and performance analysis.