Hunting monitoring is very important for high-speed trains to achieve safe operation. But all the monitoring systems are designed to detect hunting only after hunting has developed sufficiently. Under these circumstances, some damage may be caused to the railway track and train wheels. The work reported in this paper aims to solve the detection problem of small amplitude hunting before the lateral instability of high-speed trains occurs. But the information from a single sensor can only reflect the local operation state of a train. So, to improve the accuracy and robustness of the monitoring system, a multi-sensor fusion framework for detecting small amplitude hunting of high-speed trains based on an improved Dempster–Shafer (DS) theory is proposed. The framework consists of a series of steps. Firstly, the method of combining empirical mode decomposition and sample entropy is used to extract features of each operation condition. Secondly, the posterior probability support vector machine is used to get the basic probability assignment. Finally, the DS theory improved by the authors is proposed to get a more accurate detection result. This framework developed by the authors is used on high-speed trains with success and experimental findings are provided. This multi-sensor fusion framework can also be used in other condition monitoring systems on high-speed trains, such as the gearbox monitoring system, from which nonstationary signals are acquired too.
CHOICE OF SENSOR FUSION FRAMEWORK FOR TRAIN POSITIONING SYSTEM
