A fault diagnosis method for train bearings based on improved generative adversarial network

Abstract

The invention discloses a train bearing fault diagnosis method based on an improved generative confrontation network, comprising the following steps: step 1, making a real data set; step 2, constructing a generative confrontation network; Balance the data set; step 5, build a fault classifier; step 6, train the fault classifier; the present invention builds a generative adversarial network of small sample data categories through a GAN training method based on a discriminant model and a generative model, and uses a real data set to Training can improve the diagnosis and recognition rate of faults; feature extraction of samples through deep convolutional neural networks to achieve feature learning of different faults; Pearson correlation coefficient is used to measure the loss function between the generated samples and the average value of real samples to improve The quality of the generated data is closer to the real data, so that the model can extract effective multi-scale deep features.

Description

technical field [0001] The invention relates to the technical field of mechanical equipment fault diagnosis, in particular to a train bearing fault diagnosis method based on an improved generative confrontation network. Background technique [0002] At present, many feature-based fault diagnosis methods for train bearings are widely used in modern industry; in traditional fault diagnosis methods, all features are extracted manually, which inevitably relies on expert knowledge and brings a certain degree of randomness ; In recent years, with the development of artificial intelligence algorithms, deep learning has been developed in various researches due to its strong ability to directly extract useful information from signals; [0003] Although great progress has been made in intelligent fault diagnosis, its success often relies on balancing datasets, which is difficult to satisfy; on the one hand, train bearings run in a normal state most of the time, leading to the problem ...

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Problems solved by technology

[0007] Chinese patent CN201811082332.3 discloses a GAN-based interpolation method for the missing data of wind turbine hub wind measurement, applying the generative confrontation network in the field of fault diagnosis for wind turbines, and inputting training samples into the generative model and discriminant model for iterative training To achieve Nash equilibrium, the supplementary work of data is completed through the trained model, but this method only uses the generative confrontation network to generate virtual data and add it to the data set, only considering the correlation between real data and generated data, It does not take into account the classification of faults, and does not design a matching fault diagnosis model

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