Remaining useful life prediction of wind turbine generator based on 1D-CNN and Bi-LSTM. (October 2022)

• Record Type: Journal Article
• Authors: Xiao, Li; Zhang, Liyi; Niu, Feng; Su, Xiaoqin; Song, Wenqiang
• Is Part Of: International journal of fatigue. Volume 163(2022)
• Publication Date: 2022-10
• Subjects: Remaining useful life -- Wind turbine generators -- Performance degradation prediction -- 1-dimensional convolution neural network -- Bidirectional long short memory; Materials -- Fatigue -- Periodicals; Materials -- Fatigue; Periodicals; 620.1122
• DOI: 10.1016/j.ijfatigue.2022.107051
• Languages: English
• ISSNs: 0142-1123

Abstract

Highlights: The RUL prediction method for samples with unknown the performance degradation. Extracted deep features to describe the performance degradation of the WTG. Combined the repeat segmentation and the sliding window processing to generate samples. Abstract: The remaining useful life (RUL) prediction has an important guiding role in the preventive maintenance of wind turbine generators (WTGs). In this article, a real-time dynamic perception model of the RUL prediction is proposed for WTGs, which contains the multi-state parameters processing, the performance degradation analysis, the performance degradation prediction and the RUL prediction. First, the degradation process is fitted to the random distribution on the basis of the principal component analysis (PCA) of the multi-state parameters. Secondly, the multivariate time series samples and the corresponding performance degradation amount are all brought into the 1-dimensional convolution neural network (1D-CNN) for the regression analysis. Then, the bidirectional long short memory (Bi-LSTM) neural network is set to predict the amount of performance degradation in time series to obtain the future trend of performance degradation. Finally, the result of the RUL prediction is obtained by contrasting the setting threshold of the performance degradation. The results show that the proposed model has lower regression analysis error and degradation prediction error than the single deep learning and traditional models, and can obtain more accurate and reliable RUL prediction results.