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

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

Renewable Energy, 2017, vol. 103, issue C, 448-467

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.

Keywords: Fault diagnosis; Generator bearing; Nonlocal means; Order tracking (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:103:y:2017:i:c:p:448-467

DOI: 10.1016/j.renene.2016.11.054

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