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Condition Monitoring of Wind Turbine Main Bearing Based on Multivariate Time Series Forecasting

Author

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  • Xiaocong Xiao

    (School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
    School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Jianxun Liu

    (School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Deshun Liu

    (School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Yufei Tang

    (Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA)

  • Fan Zhang

    (School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

Abstract

Condition monitoring and overheating warnings of the main bearing of large-scale wind turbines (WT) plays an important role in enhancing their dependability and reducing operating and maintenance (O&M) costs. The temperature parameter of the main bearing is the key indicator to characterize the normal or abnormal operating condition. Therefore, forecasting the trend of temperature change is critical for overheating warnings. To achieve forecasting with high accuracy, this paper proposes a novel model for the WT main bearing, named stacked long-short-term memory with multi-layer perceptron (SLSTM-MLP) by utilizing supervisory control and data acquisition (SCADA) data. The model is mainly composed of multiple LSTM cells and a multi-layer perceptron regression layer. By combining condition parameters into a characteristic matrix, SLSTM can mine nonlinear, non-stationary dynamic feature relationships between temperature and its related variables. To evaluate the performance of the SLSTM-MLP model, experimental analysis was carried out from three aspects: different sample capacity sizes, different sampling time segments, and different sampling frequencies. Furthermore, the model’s capability of online fault detection was also carried out by simulation. The results of comparative studies and online fault simulation tests show that the proposed SLSTM-MLP has better performance for temperature forecasting of the main bearing of large-scale WTs.

Suggested Citation

  • Xiaocong Xiao & Jianxun Liu & Deshun Liu & Yufei Tang & Fan Zhang, 2022. "Condition Monitoring of Wind Turbine Main Bearing Based on Multivariate Time Series Forecasting," Energies, MDPI, vol. 15(5), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1951-:d:766059
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    References listed on IDEAS

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    1. Choe, Do-Eun & Kim, Hyoung-Chul & Kim, Moo-Hyun, 2021. "Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades," Renewable Energy, Elsevier, vol. 174(C), pages 218-235.
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    3. Kevin Leahy & Colm Gallagher & Peter O’Donovan & Ken Bruton & Dominic T. J. O’Sullivan, 2018. "A Robust Prescriptive Framework and Performance Metric for Diagnosing and Predicting Wind Turbine Faults Based on SCADA and Alarms Data with Case Study," Energies, MDPI, vol. 11(7), pages 1-21, July.
    4. Yang, Wenxian & Court, Richard & Jiang, Jiesheng, 2013. "Wind turbine condition monitoring by the approach of SCADA data analysis," Renewable Energy, Elsevier, vol. 53(C), pages 365-376.
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    Cited by:

    1. Alessandro Murgia & Robbert Verbeke & Elena Tsiporkova & Ludovico Terzi & Davide Astolfi, 2023. "Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis," Energies, MDPI, vol. 16(2), pages 1-20, January.
    2. Zheng Wang & Peng Gao & Xuening Chu, 2022. "Remaining Useful Life Prediction of Wind Turbine Gearbox Bearings with Limited Samples Based on Prior Knowledge and PI-LSTM," Sustainability, MDPI, vol. 14(19), pages 1-22, September.
    3. Ravi Kumar Pandit & Davide Astolfi & Isidro Durazo Cardenas, 2023. "A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines," Energies, MDPI, vol. 16(4), pages 1-17, February.
    4. Xiaocong Xiao & Jianxun Liu & Deshun Liu & Yufei Tang & Shigang Qin & Fan Zhang, 2022. "A Normal Behavior-Based Condition Monitoring Method for Wind Turbine Main Bearing Using Dual Attention Mechanism and Bi-LSTM," Energies, MDPI, vol. 15(22), pages 1-17, November.
    5. Junshuai Yan & Yongqian Liu & Xiaoying Ren, 2023. "An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm," Energies, MDPI, vol. 16(10), pages 1-23, May.

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