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A novel integral extension LMD method based on integral local waveform matching

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Abstract

This paper proposed a novel integral extension local mean decomposition (IELMD) method, which can be widely applied in wind turbine fault diagnosis. Firstly, the characteristic waveform and its corresponding integral values are calculated. Then, the similar waveform is established according to signal extreme points and their integral values. The similar waveform and characteristic waveform are compared in order to obtain an optimal waveform, which can match the characteristic waveform in the best way. Finally, the optimal waveform is used to extend the left side and right side of the original signal. According to the simulation experimental analysis, the novel IELMD method is reasonable and effective in suppressing LMD end effect, and this method can be applied in wind turbine fault diagnosis.

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Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grant No. 51305177), the Natural Science Foundation of Jiangsu Province (Grant No. BK20140238, BK20130229), the Natural Science Foundation of the Jiangsu Higher Education Institutions (Grant No. 14KJB460014), the Scientific Research Innovation key project for masters of Jiangsu Normal University (Grant No. 2014YZD017). We are also grateful to the anonymous referees and the editor-in-chief for their suggestions to improve this paper.

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, Jiangsu Normal University, Xuzhou, 221116, People’s Republic of China

    W. Y. Liu, L. Q. Zhou, N. N. Hu, Z. Z. He, C. Z. Yang & J. L. Jiang

  2. Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA

    W. Y. Liu

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  1. W. Y. Liu
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  2. L. Q. Zhou
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  3. N. N. Hu
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  4. Z. Z. He
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  5. C. Z. Yang
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  6. J. L. Jiang
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Correspondence to W. Y. Liu.

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Liu, W.Y., Zhou, L.Q., Hu, N.N. et al. A novel integral extension LMD method based on integral local waveform matching. Neural Comput & Applic 27, 761–768 (2016). https://doi.org/10.1007/s00521-015-1894-z

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  • DOI: https://doi.org/10.1007/s00521-015-1894-z

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