Health Status Assessment for New Urban Rail Vehicle Traction Systems Based on Cross Entropy and SVM

Abstract

A health status assessment method based on cross entropy and support vector machine (SVM) is proposed for the new urban rail vehicle traction systems. First, an index system for health assessment of the traction system is established, and combined weights of the index layer are obtained via cross entropy. Then, an SVM assessment model considering actual operating data and each status level of the traction system is established. Finally, the model is simulated in Matlab to obtain assessment results. The results indicate that the proposed method can provide the health status information of the traction system intuitively and complete the health status assessment of the traction system of the new urban rail vehicle effectively, by exploiting the traction system's layered analysis model. The health status can be assessed accurately and reliably by adopting the cross entropy theory and SVM theory.

A Health Status Assessment Method of Fluid Loop System Based on Hierarchical Multi-Information Fusion

“…These methods make full use of the advantages of machine learning and artificial intelligence. The application of linear regression, support vector machine (SVM) [16][17][18], support vector data description [19][20][21], neural network, and deep learning theory [22][23][24] has strongly promoted the development of health index evaluation research.…”