A Method for Recovering Missing Data of Bearing Faults of Wind Turbine Generator Based on Four-dimensional Tensor Model Characteristic Decomposition
SHI Peiming1,SUN Hangxuan1,XU Xuefang1,HAN Dongying2
1. Key Laboratory of Measurement Tech and Instrument of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004,China
2. School of Vehicles and Energy, Yanshan University, Qinhuangdao, Hebei 066004,China
Abstract：To solve the problem that the fault type cannot be identified due to missing data in the process of collecting fault information of wind turbine bearing, a method based on four-dimensional tensor model feature decomposition is proposed to recover missing data. Firstly, the fourth order tensor of vibration fault data is constructed based on the four dimensions of rotational speed, time-domain window, empirical mode decomposition and time.Secondly, the factor matrix is obtained by Tucker decomposition of the tensor. Then, tensor filling is realized by weighted optimization algorithm to recover the missing values of bearing fault data.Finally, the core tensor and factor matrix are obtained iteratively based on the gradient optimization algorithm, and the four-dimensional tensor is reconstructed to get the recovered data. The validity and reliability of the proposed method are verified by experimental and practical data. The results show that the RMSE values of the two groups of recovered data are 0.3169 and 0.0291, which are much lower than the RMSE values of the four comparison methods. By using BSR to extract fault features from two groups of recovered data, the signal-to-noise ratio is significantly improved to -13.2647 and -15.5212, respectively, which further verifies the accuracy of the proposed method.
时培明,孙航璇,许学方,韩东颖. 基于四维张量特征分解的风电机组轴承故障缺失数据恢复方法研究[J]. 计量学报, 2024, 45(5): 738-746.
SHI Peiming,SUN Hangxuan,XU Xuefang,HAN Dongying. A Method for Recovering Missing Data of Bearing Faults of Wind Turbine Generator Based on Four-dimensional Tensor Model Characteristic Decomposition. Acta Metrologica Sinica, 2024, 45(5): 738-746.
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2024, Vol. 45 
