Abstract Summary
The comfort and stability of maglev train depends on the suspension gap between bogie and rail, which should be strictly restricted in a certain range, usually 8 to 10 millimeters, by using the suspension controller. However, this restriction may become unstable due to the influence of external disturbance like the suspension controller failure. When such failure appears, the electromagnetic force will suddenly change, lead to the suspension gap out of balance, and even cause a collision between bogie and rail. To keep the operation of maglev train, this study aims to analyze and identify the maglev suspension controller failure. With a group of accelerometers installed on rail, the acceleration data that represents the structural dynamic response of rail, can be collected to observe the failure pattern of maglev suspension controller. In general, the failure pattern is analyzed by the dynamic characteristics of acceleration data from both perspective of time and frequency domain. Then, to realize the intelligent identification of such failure, a method based on convolution neural network is developed to distinguish the normal and failed suspension controller. It is found that there is a significant high frequency component at a certain moment when the suspension controller fails, while this phenomenon is absent in a normal suspension controller. Based on this discriminative feature, the convolution neural network enables the accurate binary classification on suspension controller failure. As a result, the findings from this study are expected to condition monitoring on suspension controller.
