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Specialization improved nonlocal means to detect periodic impulse feature for generator bearing fault identification

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  • Chen, Jinglong
  • Pan, Jun
  • Zhang, Chunlin
  • Luo, Xiaoyu
  • Zhou, Zitong
  • Wang, Biao

Abstract

It is significant to perform damage identification of wind turbine using running condition data for guaranteeing its safe operation. Because acquired condition data is usually mixed with heavy background noise, feature enhancement and noise elimination method is necessary for this task. Nonlocal means algorithm increase the signal to noise ratio without destroying the original frequency spectrum structure, which can reserve the useful information farthest and meanwhile eliminate noise. And it seems to be a possible powerful tool along with demodulation technique (under constant speed condition) or order tracking analysis method (under variable speed condition) for the damage identification. However, the actual application cases show that it would obtain some not entirely satisfying results when face strong background noise situation. So, the specialization improved nonlocal means method is developed for the damage identification of generator bearing. Based on the analyzing the essence characteristic of mechanical vibration signal, more reasonable ideas on the algorithm design such as neighborhood selection and variation of weighting function during nonlocal means denoising for this task are proposed. The effectiveness of specialization improved nonlocal means method is verified by fault identification cases study including variable speed condition.

Suggested Citation

  • Chen, Jinglong & Pan, Jun & Zhang, Chunlin & Luo, Xiaoyu & Zhou, Zitong & Wang, Biao, 2017. "Specialization improved nonlocal means to detect periodic impulse feature for generator bearing fault identification," Renewable Energy, Elsevier, vol. 103(C), pages 448-467.
    • Handle: RePEc:eee:renene:v:103:y:2017:i:c:p:448-467
    DOI: 10.1016/j.renene.2016.11.054
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    References listed on IDEAS

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    1. Chen, Jinglong & Pan, Jun & Li, Zipeng & Zi, Yanyang & Chen, Xuefeng, 2016. "Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals," Renewable Energy, Elsevier, vol. 89(C), pages 80-92.
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    5. 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. Tang, Yaochi & Chang, Yunchi & Li, Kuohao, 2023. "Applications of K-nearest neighbor algorithm in intelligent diagnosis of wind turbine blades damage," Renewable Energy, Elsevier, vol. 212(C), pages 855-864.
    2. Miao, Yonghao & Zhao, Ming & Liang, Kaixuan & Lin, Jing, 2020. "Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal," Renewable Energy, Elsevier, vol. 151(C), pages 192-203.

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