Research on Turnout Fault Diagnosis Algorithm Based on CNN-GRU-Attention
Turnout is one of the railway signal infrastructures that affects the safety of trains.By ana-lyzing the power data of the turnout operation process,the operation status of the turnout can be effectively judged.In order to achieve automatic,efficient and accurate diagnosis of turnout faults,a fault diagnosis method based on deep learning is studied and proposed.The study first utilizes convolutional neural net-works to extract spatial features from data,then calls on gated recurrent unit networks to extract temporal features,introduces attention mechanisms for allocating weights to features,and finally uses Softmax clas-sifiers for classification.In comparative experiments,multiple indicators are used to evaluate the perform-ance of this method,and the results show that this method has significant advantages in diagnostic per-formance compared to the basic methods and two other existing methods.
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