Engine fault diagnosis method based on bp neural network

Abstract

The invention discloses a method for diagnosing engine faults based on BP neural network, comprising (1) collecting engine fault data and listing the causes of engine faults; (2) determining the optimal hidden layer node number of BP neural network model, and establishing Neural network model; (3) Train the BP neural network model according to the existing fault data; (4) Use the trained BP neural network model to analyze the collected engine data to determine the cause of the fault corresponding to the data. In the past, engine fault diagnosis had defects such as complex mechanism, low detection accuracy, high cost, and failure to display the cause of the fault. The present invention is mainly used in engine fault diagnosis and diagnosis, which is more advantageous than previous methods, saves costs, and improves modeling Efficiency, can quickly lock the optimal number of hidden layer nodes.

Description

technical field [0001] The invention relates to an engine fault diagnosis method, in particular to an engine fault diagnosis method based on BP neural network. Background technique [0002] With the continuous development of artificial intelligence and machine algorithms, fault detection methods based on artificial neural networks are more and more used to solve complex fault diagnosis problems than traditional diagnostic methods. For the complex structure of the engine, before the neural network is not combined, fault diagnosis is difficult and there are many processes. And the neural network is used to train the data in order to get the processing results quickly, and the effect of predicting the fault is better. Especially for the complex and cumbersome problem of engine fault diagnosis, the traditional method cannot reduce the process, but the neural network method can quickly locate and predict the problem point. However, for a neural network topology, the input and o...

AI Technical Summary

Problems solved by technology

Compared with the exhaustive method, the existing golden section method and dichotomy method reduce a lot of workload, but they have the disadvantages of slow convergence speed and low efficiency

For the dichotomy method, the problem of increasing verification points due to the interval convergence point is unavoidable; while the number of iteration steps of the golden section method cannot guarantee simplification

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