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Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theory

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Abstract

The gearbox is an important component in industrial drives, providing safe and reliable operation for industrial production. Wavelet packet transform (WPT) analysis was used to extract fault features in the vibration signals generated by a gearbox. The extracted features from the WPT were used as input in a rough set (RS) for attribute reduction and then combined with a genetic algorithm to obtain global optimal attribute reduction results. The fault features gained after the attribute reductions were used to generate decision rules. The unknown gear status signal attributes were used as input to match the generated decision rules for fault diagnosis purposes. Gearbox vibration signals contain a significant amount of gear status information; a WPT has an acute portion-locked ability to extract attribute information from the vibration signals. However, WPT frequency aliasing would lead to the generation of spurious frequency components, affecting gear fault diagnosis. In this paper, we introduce an improved WPT to eliminate frequency aliasing, thus improving the accuracy of fault diagnosis. This paper studies the use of wavelet packet for feature extraction and the RS for classification; the results demonstrate that this method can accurately and reliably detect failure modes in a gearbox.

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Acknowledgments

This research was supported by the National Natural Science Foundation of China (51175102) and the Fundamental Research Funds for the Central Universities (Grant No. HIT.NSRIF.201638).

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Authors and Affiliations

  1. School of Mechatronics Engineering, Harbin Institute of Technology, 92 West dazhi street, Harbin, 150001, China

    Wentao Huang, Fanzhao Kong & Xuezeng Zhao

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  1. Wentao Huang
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  2. Fanzhao Kong
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  3. Xuezeng Zhao
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Correspondence to Wentao Huang.

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Huang, W., Kong, F. & Zhao, X. Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theory. J Intell Manuf 29, 1257–1271 (2018). https://doi.org/10.1007/s10845-015-1174-x

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  • DOI: https://doi.org/10.1007/s10845-015-1174-x

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