A mechanical equipment fault intelligent diagnosis method based on a generation model under small sample data

Abstract

The invention discloses a mechanical equipment fault intelligent diagnosis method based on a generation model under small sample data. The mechanical equipment fault intelligent diagnosis method comprises: carrying out zero-mean-value standardization preprocessing on a small number of obtained mechanical signals; Establishing a composite network for mechanical signal generation; Training a generative adversarial network model in an adversarial manner in combination with a Wasserstein distance and a gradient punishment method; Establishing a deep convolutional neural network model for classifying the operation states of the mechanical equipment by using the mechanical signals; And by combining the generative adversarial composite neural network model and the deep convolutional neural network model, training two networks by using a small amount of real mechanical signals, and finally realizing intelligent fault diagnosis of the mechanical equipment under small sample data. The method hasthe advantages of being good in mechanical signal feature extraction effect, high in state classification accuracy and good in mechanical signal data expansion performance.

Description

technical field [0001] The invention relates to the field of fault diagnosis of mechanical equipment, in particular to an intelligent fault diagnosis method for mechanical equipment based on a generated model under small sample data. Background technique [0002] During the operation of mechanical equipment, its main components, such as bearings, gears, rotors, etc., are prone to failure due to the continuous load, resulting in economic losses and casualties. In order to reduce the loss caused by the failure of mechanical equipment, it is necessary to carry out research on fault diagnosis and condition monitoring of mechanical equipment. All kinds of mechanical signals collected on mechanical equipment under actual working conditions will be polluted by noise, making it difficult to effectively extract features and identify states of mechanical signals. Denoising and feature extraction of mechanical signals is generally regarded as the main task and main difficulty of mecha...

AI Technical Summary

Problems solved by technology

The small sample problem seriously affects the timeliness and accuracy of fault diagnosis and condition monitoring of mechanical equipment, so it is necessary to carry out research on fault diagnosis of mechanical equipment under small sample problems

[0004] Traditionally, the way to expand the data set in the fault diagnosis process is oversampling, but oversampling is only reusing the only small amount of fault sample information, which is not general

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