Train control onboard device failure diagnosis method with LSTM (Long Short Term Memory Network) and neural network combined

Abstract

The invention provides a train control onboard device failure diagnosis method with a LSTM (Long Short Term Memory Network) and a neural network combined. The method comprises steps: a log file of theonboard device is used to build an onboard device operation information corpus through text data mining processing, and original sample data are built; a multilayer network system with the LSTM and the BP (back propagation) network cascaded is built, and a Bayesian regularization algorithm is adopted to optimize the multilayer network system; training sample data are used to train the optimized multilayer network system, the well-trained multilayer network system is used to build a failure diagnosis model for the train control onboard device, the failure diagnosis model is used to diagnose anunknown failure sample of the train control onboard device, and a diagnosis result of the unknown failure sample is obtained. According to the train control onboard device failure diagnosis method with the LSTM and the BP network cascaded, intelligent train operation information classification is realized, demands on manual experience in the field are reduced, and failure diagnosis on the train control onboard device is carried out effectively.

Description

technical field [0001] The invention relates to the technical field of train fault diagnosis, in particular to a fault diagnosis method for train control vehicle-mounted equipment using a long-short-term memory network combined with a neural network. Background technique [0002] The typical BP (back propagation, neural network) structure learning and training process is the process in which input information is transmitted from the input layer to the output layer through the hidden layer. The special feature of BP is that when the actual output value does not reach the expected value, the connection weights of neurons in each layer can be modified through error backpropagation, and the training continues until the output error is within the allowable range. After the network learning and training, the connection weights between neurons represent the specific knowledge of the diagnostic object. [0003] The forward propagation process of the neural network is only used to c...

AI Technical Summary

Problems solved by technology

[0005] At present, the fault diagnosis and positioning of train control on-board equipment in the prior art mainly rely on the manual diagnosis of a large number of maintenance personnel, which is time-consuming and laborious, and the diagnosis accuracy is low

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