Support Vector Machine-Based Fault Diagnosis under Data Imbalance with Application to High-Speed Train Electric Traction Systems

Abstract

The safety and reliability of high-speed train electric traction systems are crucial. However, the operating environment for China Railway High-speed (CRH) trains is challenging, with severe working conditions. Dataset imbalance further complicates fault diagnosis. Therefore, conducting fault diagnosis for high-speed train electric traction systems under data imbalance is not only theoretically important but also crucial for ensuring vehicle safety. Firstly, when addressing the data imbalance issue, the fault diagnosis mechanism based on support vector machines tends to prioritize the majority class when constructing the classification hyperplane. This frequently leads to a reduction in the recognition rate of minority-class samples. To tackle this problem, a self-tuning support vector machine is proposed in this paper by setting distinct penalty factors for each class based on sample information. This approach aims to ensure equal misclassification costs for both classes and achieve the objective of suppressing the deviation of the classification hyperplane. Finally, simulation experiments are conducted on the Traction Drive Control System-Fault Injection Benchmark (TDCS-FIB) platform using three different imbalance ratios to address the data imbalance issue. The experimental results demonstrate consistent misclassification costs for both the minority- and majority-class samples. Additionally, the proposed self-tuning support vector machine effectively mitigates hyperplane deviation, further confirming the effectiveness of this fault diagnosis mechanism for high-speed train electric traction systems.