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A novel wind turbine fault diagnosis method based on intergral extension load mean decomposition multiscale entropy and least squares support vector machine

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  • Gao, Q.W.
  • Liu, W.Y.
  • Tang, B.P.
  • Li, G.J.

Abstract

Aimed at the non-stationary and nonlinear characteristics of wind turbine vibration signals, a novel fault diagnosis method based on integral extension load mean decomposition multiscale entropy and least squares support vector machine was proposed in this paper. At first, the raw vibration signals monitored from the wind turbine were divided into groups for the pre-process. Then the signals were processed in groups with integral extension load mean decomposition method and Product Functions were obtained. The characteristic parameters were achieved by multiscale entropy method of processing main Product Functions, which described the signal characteristics. Finally, the characteristic parameters were entered into least squares support vector machine, and least squares support vector machine was trained. Next the trained least squares support vector machine was tested and the pattern was classified. The method can not only extract characteristic parameters effectively, but also classify the fault type accurately. The effectiveness and availability of the proposed method were proved in the wind turbine measured data experiment.

Suggested Citation

  • Gao, Q.W. & Liu, W.Y. & Tang, B.P. & Li, G.J., 2018. "A novel wind turbine fault diagnosis method based on intergral extension load mean decomposition multiscale entropy and least squares support vector machine," Renewable Energy, Elsevier, vol. 116(PA), pages 169-175.
    • Handle: RePEc:eee:renene:v:116:y:2018:i:pa:p:169-175
    DOI: 10.1016/j.renene.2017.09.061
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    1. Li, Zhongliang & Outbib, Rachid & Giurgea, Stefan & Hissel, Daniel & Jemei, Samir & Giraud, Alain & Rosini, Sebastien, 2016. "Online implementation of SVM based fault diagnosis strategy for PEMFC systems," Applied Energy, Elsevier, vol. 164(C), pages 284-293.
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    2. Hui Li & Bangji Fan & Rong Jia & Fang Zhai & Liang Bai & Xingqi Luo, 2020. "Research on Multi-Domain Fault Diagnosis of Gearbox of Wind Turbine Based on Adaptive Variational Mode Decomposition and Extreme Learning Machine Algorithms," Energies, MDPI, vol. 13(6), pages 1-20, March.
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    5. Lei, Jinhao & Liu, Chao & Jiang, Dongxiang, 2019. "Fault diagnosis of wind turbine based on Long Short-term memory networks," Renewable Energy, Elsevier, vol. 133(C), pages 422-432.
    6. Dhibi, Khaled & Mansouri, Majdi & Bouzrara, Kais & Nounou, Hazem & Nounou, Mohamed, 2022. "Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems," Renewable Energy, Elsevier, vol. 194(C), pages 778-787.
    7. He, Guolin & Ding, Kang & Wu, Xiaomeng & Yang, Xiaoqing, 2019. "Dynamics modeling and vibration modulation signal analysis of wind turbine planetary gearbox with a floating sun gear," Renewable Energy, Elsevier, vol. 139(C), pages 718-729.
    8. Lixiao Cao & Zheng Qian & Hamid Zareipour & David Wood & Ehsan Mollasalehi & Shuangshu Tian & Yan Pei, 2018. "Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions," Energies, MDPI, vol. 11(12), pages 1-20, November.
    9. Tan, Hongchuang & Xie, Suchao & Ma, Wen & Yang, Chengxing & Zheng, Shiwei, 2023. "Correlation feature distribution matching for fault diagnosis of machines," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    10. Li, Yanting & Liu, Shujun & Shu, Lianjie, 2019. "Wind turbine fault diagnosis based on Gaussian process classifiers applied to operational data," Renewable Energy, Elsevier, vol. 134(C), pages 357-366.
    11. Wenxin Yu & Shoudao Huang & Weihong Xiao, 2018. "Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System," Energies, MDPI, vol. 11(10), pages 1-11, September.
    12. Li, Yanting & Jiang, Wenbo & Zhang, Guangyao & Shu, Lianjie, 2021. "Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data," Renewable Energy, Elsevier, vol. 171(C), pages 103-115.

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