Incipient Fault Detection for Air Brake System of High-Speed Trains

Abstract

The ideal air brake system is to provide accurate brake pressures, which ensures the normal braking performance of high-speed trains. For the high-speed trains in China, a univariate monitoring strategy has been adopted to monitor the state of each brake cylinder of the air brake system separately. In practice, this kind of strategy is effective for faults with large amplitude, but it is always ineffective for all kinds of incipient faults with small amplitude. Due to nonstationary and multimode properties inherent in air brake systems, conventional detection methods are difficult to be used. In this article, we develop novel detection strategies for three kinds of incipient faults in the air brake systems, including sensor deviation fault, leakage fault, and brake cylinder component fault. Starting from the characteristics of actual data from air brake systems, some variables with high level of consistency are selected. Then, a linear data transformation is conducted to obtain approximate stationary properties, and all the data in multiple modes are mapped into a compact domain. In addition, a fault-to-signal ratio (FSR) is proposed to reflect the sensitivity of the proposed detection statistic to the fault. In particular, the multistage control limits and sliding window strategies are integrated into the detection statistic, which effectively lowers the control limits and enhances robustness to noise, respectively. Finally, experiments on the braking test platform are provided to show the effectiveness of our proposed strategies.