Mechanical intelligent fault prediction method based on automatic convolutional neural network

Abstract

The invention provides a mechanical intelligent fault prediction method based on an automatic convolutional neural network, and the method comprises the steps: obtaining an equipment fault signal, carrying out the preprocessing of the equipment fault signal, and obtaining a preprocessed fault signal; constructing an automatic convolutional neural network ACNN fault diagnosis model, wherein the ACNN fault diagnosis model comprises a group of convolutional neural networks CNN and a group of deep deterministic strategy gradient networks DDPG, the convolutional neural network CNN is used for equipment fault prediction, and the deep deterministic strategy gradient network DDPG is used for realizing automatic adjustment of three parameters of learning rate, batch and regularization of the convolutional neural network CNN; training the ACNN fault diagnosis model by using the preprocessed fault signal to obtain a trained ACNN fault diagnosis model; and applying the trained fault diagnosis model to equipment fault diagnosis. The beneficial effects of the invention are that the method achieves the automatic adjustment and optimization of the parameters of the convolutional neural network, and enables the convolutional neural network to be good in fault feature extraction capability.

Description

technical field [0001] The invention relates to the field of fault prediction, in particular to a machine intelligent fault prediction method based on an automatic convolutional neural network. Background technique [0002] With the improvement of equipment intelligence, knowledge-based fault diagnosis methods, also known as data-driven fault diagnosis methods, have gradually been studied by a large number of scholars, and have achieved a large number of application effects on a large number of equipment, such as bearings, aviation Engines, large wind turbines, etc. [0003] Deep learning is the core of the data-driven fault diagnosis method, which is mainly used for the analysis and processing of equipment signals, and realizes the mining and prediction of equipment abnormal states. However, the performance of data-driven fault diagnosis methods represented by deep learning directly depends on the choice of hyperparameters. Although a large number of default hyperparamete...

AI Technical Summary

Problems solved by technology

The reasonable selection of the three most important parameters of the convolutional neural network (learning rate, batch size, and regularization value) will directly affect the extraction effect of the convolutional neural network on fault features, thereby reducing the prediction accuracy and generalization of the fault diagnosis model. ability

At the same time, in the existing technology, the selection of the above three parameters is often modulated by manual experience or repeated experiments, which requires a lot of manual energy and has uncertainty.

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