Fault prediction method based on migrated convolutional neural network

Abstract

The invention belongs to the field of fault prediction of neural networks, and discloses a fault prediction method based on a migrated convolutional neural network. The method comprises the followingsteps: (a) numbering fault types, collecting a time-domain signal of a to-be-predicted object, acquiring initial fault type numbers, and converting time-domain signals into RGB images; (b) initializing an FC layer of a deep residual network model, and adding a classifier to obtain an improved network model; (c) inputting RGB images into the network model to train the FC layer and the classifier, continuously updating weight values of the FC layer, and when obtained fault type numbers are close to the initial fault type numbers, determining that the corresponding weight values are needed new weight values, and completing migration of the network model; and (d) inputting the RGB image of the to-be-predicted object into the migrated convolutional neural network model, and outputting a predicted fault type number. Through the method, a structure of the adopted migrated convolutional neural network model is simple, a prediction speed is high, and a prediction result is accurate.

Description

technical field [0001] The invention belongs to the field of neural network fault prediction, and more specifically relates to a fault prediction method based on a migration convolutional neural network. Background technique [0002] In recent years, many researchers have studied fault prediction. As a typical fault prediction method, data-driven fault prediction can use historical data to establish fault modes without any explicit models or signal symptoms, which is very suitable for complex systems. , with the rapid development of intelligent manufacturing, the data generated by machinery and equipment has been well improved and collected. Mechanical big data has brought new opportunities for the manufacturing industry to achieve a trouble-free process. Data-driven failure prediction is becoming more and more popular. The attention of researchers and engineers is critical to finding more robust data-driven failure prediction methods. [0003] Learning from a large amount ...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the above defects or improvement needs of the prior art, the present invention provides a fault prediction method based on the migration convolutional neural network. The method first converts the time-domain signal into an RGB image, and then migrates the convolutional neural network to obtain The migrated convolutional neural network, and then use the migrated convolutional neural network to predict faults, thus solving the technical problem of low fault prediction accuracy

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