Research on Fault Diagnosis Method for Wind Turbine Bearings Based on DTCWT and Deep Residual Networks
With the rapid development of wind power technology,the reliability and performance of wind turbines have become a focal point for efficient and continuous operation in wind power projects.Bearings,as critical mechanical components in wind turbines,are prone to wear during operation.Therefore,timely diagnosis and maintenance of bearing conditions are crucial.This paper presents a Wind Turbine Generator Bearing Fault Diagnosis Model called DTRSANMD based on the combination of dual-tree complex wavelet transform and deep residual networks.Firstly,the vibration signals of wind turbine generators are subjected to multiscale decomposition using dual-tree complex wavelet transform.Subsequently,the extracted feature information is input into a deep residual network incorporating attention mechanisms to obtain effective deep feature representations.Finally,the introduction of the multi-kernel maximum mean discrepancy method evaluates the feature distribution under variable operating conditions,effectively reducing distribution differences between the source and target domains,and thereby enhancing the model's generalization performance.In conclusion,a Wind Turbine Generator Multi-Source Data Intelligent Collection Analysis Terminal and System is designed,to enable remote diagnosis and analysis of mechanical components in wind turbines.This implementation contributes to the reduction of maintenance costs.
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