Train bearing fault diagnosis method based on improved generative adversarial network

Abstract

The invention discloses a train bearing fault diagnosis method based on an improved generative adversarial network. The method comprises the following steps of 1, making a real data set; step 2, constructing a generative adversarial network; step 3, training the generative adversarial network; 4, making a balanced data set; step 5, building a fault classifier; step 6, training a fault classifier; according to the method, the generative adversarial network of small sample data categories is built through the GAN training method based on the discriminant model and the generative model, and the generative adversarial network is trained by adopting the real data set, so that the fault diagnosis and recognition rate can be improved; performing feature extraction on the sample through a deep convolutional neural network to realize feature learning of different faults; the generation quality is improved by measuring the loss function between the average values of the generated sample and the real sample through the Pearson's correlation coefficient, and 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 industries; in traditional fault diagnosis methods, all features are manually extracted, 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 intelligent fault diagnosis has made great progress, its success often relies on a balanced dataset, which is difficult to satisfy; on the one hand, train bearings run in a normal state most of the time, leading to the problem of unb...

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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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