Deep adversarial diagnosis method for fan bearing fault under non-equilibrium small sample scene

Abstract

The invention provides a deep adversarial diagnosis method for a fan bearing fault under a non-equilibrium small sample scene. The deep adversarial diagnosis method is characterized by comprising thefollowing steps of acquiring wind turbine bearing vibration signals, building an improved AC-GAN (Generative Adversarial Networks) model, building an improved AC-GAN sample, generating a wind turbinebearing vibration signal sample, and diagnosing wind turbine bearing faults under various scenes. The deep adversarial diagnosis method solves the problems of complex vibration signal noise interferences, fewer fault samples, and unbalanced number of samples between categories in the fan fault diagnosis based on the vibration signals, improves the fault identification accuracy under a small samplenon-equilibrium scene, has good fault identification accuracy under complex scenes such as high noise interferences, insufficient number of samples and nonequilibrium of different types of sample training set scales, has the advantages of being scientific and reasonable, strong in adaptability, high in practical value, and can provide references for fan research and development, wind farm operation and maintenance, wind turbine research and other related personnel.

Description

[0001] The invention relates to a fault diagnosis method for fan bearings, in particular, a deep confrontation diagnosis method for fan bearing faults in an unbalanced small-sample scenario, which is applied to online diagnosis of mechanical fault states of wind turbine bearings. Background technique [0002] Bearings, as the core components of the wind turbine transmission system, are continuously affected by alternating impact forces and loads, and become parts with high incidence of mechanical failures. Once the bearing is damaged, the downtime of the fan will be long, the repair cost will be high, and the economic loss will be serious. Therefore, accurate diagnosis of wind turbine bearing faults is of great significance to ensure the safe and reliable operation of wind turbines and the economy of wind farms. Existing fan bearing fault diagnosis is generally carried out based on bearing vibration signals, and the diagnosis process is divided into two parts: feature extracti...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art, to provide a scientific and reasonable, strong adaptability, high practical value, capable of high noise interference, insufficient number of samples and unbalanced training set scale of different types of samples, and complex scenarios, In-depth adversarial diagnosis method for fan bearing faults in unbalanced small sample scenarios with better fault identification accuracy

Images