A Deep Learning Method for Bogie Fault Diagnosis Considering Wheel Polygons
Current research on bogie fault diagnosis only considers ideal train operating conditions,without considering the influence of wheel polygons,the excitation of which significantly reduces the accuracy of fault diagnosis.For this reason,a deep learning method based on LSTM network and convolutional neural network is proposed.The method consists of a long short-term memory network as well as a one-dimensional convolutional neural network,and introduces an attention mechanism to emphasize the features of training data,thereby improving the accuracy of bogie fault signal recognition.Taking the bogie of CRH380 rolling stock as an example,the wheel polygon excitation is introduced,and the proposed method is used to classify the key component faults under different working conditions.By comparing with the existing methods,it is found that the proposed method,due to the introduction of the attention mechanism,overcomes the noise interference caused by wheel polygons to a greater extent,and improves the accuracy of bogie fault diagnosis.
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