Railway turnouts are a key component of the railway transportation system,and their working status directly affects the safety and efficiency of train operation,and the timely diagnosis and maintenance of railway turnout faults is very important to ensure the normal operation of the railway system.This paper proposes a deep learning fault diagnosis model based on the combination of Convolutional Neural Network(CNN)and Long Short-Term Memory Network(LSTM),which collects the current and power curve data of railway switches to form a training set and a test set,and trains and tests the model.The CNN-LSTM hybrid model proposed in this paper has a better fault diagnosis effect.Finally,a set of railway turnout fault monitoring and diagnosis system was designed and developed,which realized the real-time monitoring and fault diagnosis of railway turnouts.
维普
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