基于迁移学习的海上风电机组轴承早期故障预警策略OA北大核心CSTPCD

A transfer learning-based early fault warning method for offshore wind turbine bearings is established to address the problems of varying operating conditions of offshore wind turbines and many false alarms for early fault warning of turbine bearings.The method uses the short-time Fourier transform to extract the time-frequency domain features of the vibration signals,which are normalised to form pre-processed samples.The objective function of the convolutional autoencoder is supplemented with a support vector data description regular term and a maximum mean discrepancy regular term to constrain the feature distribution while obtaining the common features center of the bearings in normal state under different operating conditions.The Euclidean distance between the online sample features and the common feature center is calculated to construct bearing health indicator sequence,and the ADF(Augmented Dickey-Fuller)test is introduced to perform stationarity analysis and capture the sequence mutation points,which finally realize the early fault warning of bearings in offshore wind turbines.The validation on the XJTU-SY bearing dataset showed that the proposed method has fewer false alarms,high accuracy and better detection stability.