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A new gear fault feature extraction method based on hybrid time–frequency analysis

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Abstract

Gear is one of the popular and important components in the rotary machinery transmission. Vibration monitoring is the common way to take gear feature extraction and fault diagnosis. The gear vibration signal collected in the running time often reflects the characteristics such as non-Gaussian and nonlinear, which is difficult in time domain or frequency domain analysis. This paper proposed a novel gear fault feature extraction method based on hybrid time–frequency analysis. This method combined the Mexican hat wavelet filter de-noise method and the auto term window method at the first time. This method can not only de-noise noise jamming in raw vibration signal, but also extract gear fault features effectively. The final experimental analysis proved the feasibility and the availability of this new method.

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Acknowledgments

This research was supported by the Scientific research support project for teachers with doctor’s degree, Jiangsu normal university, China (Grant No. 11XLR15), the National Natural Science Foundation of China (Grant Nos. 51075347 and 51305179), the Natural Science Foundation of the Jiangsu Higher Education Institutions (Grant No. 13KJB510009).

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Authors and Affiliations

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

    Wenyi Liu, Jiguang Han & Xiangning Lu

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

    Wenyi Liu

Authors
  1. Wenyi Liu
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  2. Jiguang Han
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  3. Xiangning Lu
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Correspondence to Wenyi Liu.

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Liu, W., Han, J. & Lu, X. A new gear fault feature extraction method based on hybrid time–frequency analysis. Neural Comput & Applic 25, 387–392 (2014). https://doi.org/10.1007/s00521-013-1502-z

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  • DOI: https://doi.org/10.1007/s00521-013-1502-z

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