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Trend prediction of wear fault of wind generator high-speed gear using a fusion of UICA and PE method

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Abstract

The large wind generating set works under the varying operation conditions for years, generally the fault characteristic values of the rotary components based on the energy mode is coupled with other noises, so the fault trend can not be accurately predicted. With the wear fault of the high-speed gear of the wind generator as the research object, this paper proposes the trend prediction method of the wear fault of the high-speed gear based on the fusion of ultra-complete independent component analysis (UICA) and parameter estimation (PE), constructs the ultra-complete analysis model, separates the similar source signals more than mixing signals by using the UICA, and finds useful component with the features of the pure similar fault source signal as the basis. Based on the similarity between the similar fault source signal and fault source signal, this paper estimates the value domain of the magnification time of the similar shapes by using the PE, identifies the mapping between continuous and one-way varying magnification time domain and rotary component fault degree, establishes the fault degree judgment standard, and determines and predicts the fault degree and the fault trend based on the energy change trend diagram of the whole-lifecycle fault source signal of the high-speed gear. The above method is used to process the wear fault data of the high-speed gear. The results indicate that the above method has obvious effect in processing of the cycle sudden signals, so it indicates that this method has certain engineering application value and provides reference to solve the problem that the number of the independent vibration sources is more than it of the mixing signals in vibration analysis.

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Acknowledgements

This study was supported by grants from the National Natural Science Foundation of China (51275052 to XU Xiao-li), the Key Project of Natural Science Foundation of Beijing City (3131002 to XU Xiao-li), the National High Technology Research and Development Program (863 Program)(2015AA043702-CS01 to XU Xiao-li), the Project Supported by Beijing Municipal Education Commission (KM201611232020 to JIANG Zhang-lei), the Key Project of Science and Technique Development Plan Supported by Beijing Municipal Commission of Education(KZ201311232036 to XU Bao-jie) and the Beijing Municipal Education Commission Science and Technology Plan Project(KM201411232020 to CHEN Tao). We thanked BIAN Jia-lei and HUANG Ji for their technical assistance.

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Authors and Affiliations

  1. School of Mechanical and Vehicle Engineering, Beijing Institute of Technology, Beijing, 100081, China

    Xiwei Zhao, Xiaoli Xu & Wenxiang Zhao

  2. Key Laboratory of Modern Measurement& Control Technology of Ministry of Education, Beijing Information Science and Technology University, Beijing, 100192, China

    Xiaoli Xu, Zhanglei Jiang & Xiuli Liu

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  1. Xiwei Zhao
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  2. Xiaoli Xu
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  3. Wenxiang Zhao
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  4. Zhanglei Jiang
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  5. Xiuli Liu
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Correspondence to Xiaoli Xu.

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Zhao, X., Xu, X., Zhao, W. et al. Trend prediction of wear fault of wind generator high-speed gear using a fusion of UICA and PE method. Cluster Comput 20, 427–437 (2017). https://doi.org/10.1007/s10586-017-0733-7

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  • DOI: https://doi.org/10.1007/s10586-017-0733-7

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