Fault diagnosis of high-speed train bogie based on LSTM neural network

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Dear editor,rnWith the extensive implementation of high-speed railway ac-celeration,higher requirements for safety and stability are put forward.In order to guarantee the safety of the high-speed train(HST),it is critical to detect the abnormal state in operation.Bogie that plays an important role in reduc-ing vehicle vibration is connected to the track and the train body,and its health condition directly affects the safe oper-ation of train.rnThere is a correlation between the vibration of train body and the state of the bogie.With the deterioration of me-chanical components in the whole system,the vibration sig-nal also changes.Many sensors are installed on the HST which is tracked and monitored for a while.Then a con-siderable amount of train data will be obtained,normally containing abundant fault information.Because the prin-ciple of bogie failure is complex and the fault features are difficult to visualize,it is hard to extract fault information using traditional signal processing methods.As such,a new and effective method in dealing with fault diagnosis of train bogies is of significance.
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