A New Strategy: Remaining Useful Life Prediction of Wind Power Bearings Based on Deep Learning under Data Missing Conditions

Xuejun Li, Xu Lei, Lingli Jiang (), Tongguang Yang () and Zhenyu Ge
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Xuejun Li: The Laboratory for Rotating Vibration Monitoring and Diagnostics Technology in Mechanical Industries, Foshan University, Foshan 528000, China
Xu Lei: The Laboratory for Rotating Vibration Monitoring and Diagnostics Technology in Mechanical Industries, Foshan University, Foshan 528000, China
Lingli Jiang: The Laboratory for Rotating Vibration Monitoring and Diagnostics Technology in Mechanical Industries, Foshan University, Foshan 528000, China
Tongguang Yang: School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Zhenyu Ge: The Laboratory for Rotating Vibration Monitoring and Diagnostics Technology in Mechanical Industries, Foshan University, Foshan 528000, China

Mathematics, 2024, vol. 12, issue 13, 1-21

Abstract: With its formidable nonlinear mapping capabilities, deep learning has been widely applied in bearing remaining useful life (RUL) prediction. Given that equipment in actual work is subject to numerous disturbances, the collected data tends to exhibit random missing values. Furthermore, due to the dynamic nature of wind turbine environments, LSTM models relying on manually set parameters exhibit certain limitations. Considering these factors can lead to issues with the accuracy of predictive models when forecasting the remaining useful life (RUL) of wind turbine bearings. In light of this issue, a novel strategy for predicting the remaining life of wind turbine bearings under data scarcity conditions is proposed. Firstly, the average similarity (AS) is introduced to reconstruct the discriminator of the Generative Adversarial Imputation Nets (GAIN), and the adversarial process between the generative module and the discriminant is strengthened. Based on this, the dung beetle algorithm (DBO) is used to optimize multiple parameters of the long-term and short-term memory network (LSTM), and the complete data after filling is used as the input data of the optimized LSTM to realize the prediction of the remaining life of the wind power bearing. The effectiveness of the proposed method is verified by the full-life data test of bearings. The results show that, under the condition of missing data, the new strategy of AS-GAIN-LSTM is used to predict the RUL of wind turbine bearings, which has a more stable prediction performance.

Keywords: wind power bearing; remaining useful life prediction; data missing; average similarity (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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