Health Evaluation of MVB Based on SVDD and Sample Reduction

Abstract

Multifunction vehicle bus (MVB) is the most widely used train communication network whose performance degradation and anomaly will heavily affect the train’s safe and stable operation. However, current scheduled maintenance and post-failure maintenance of MVB cannot detect the early anomaly and evaluate the health condition of the network in time. This paper provides a method to detect the anomaly and evaluate the health condition of MVB based on a one-class classification (OCC) algorithm called density-based sample reduction for support vector data description (DBSRSVDD). First, network features are extracted from physical layer waveform parameters. In order to reduce the computational complexity of SVDD, a sample reduction operation is conducted to screen out the edge samples as support vector candidates. Then, the SVDD models representing the normal patterns of a single MVB node are trained based on the support vector candidates. Performance degradation of the node is quantified by the distance between the tested sample and the trained hyper sphere. The whole network’s health condition is the linear weighted sum of the nodes’ scores based on their bandwidth occupancy. The experimental results show that the proposed method can detect the anomaly and degradation of MVB successfully, improve accuracy, and reduce training time compared with the existing methods.