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

Author

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  • Wentao Huang

    (Harbin Institute of Technology)

  • Fanzhao Kong

    (Harbin Institute of Technology)

  • Xuezeng Zhao

    (Harbin Institute of Technology)

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.

Suggested Citation

  • Wentao Huang & Fanzhao Kong & Xuezeng Zhao, 2018. "Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theory," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1257-1271, August.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:6:d:10.1007_s10845-015-1174-x
    DOI: 10.1007/s10845-015-1174-x
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    Cited by:

    1. Guo, Sheng & Yang, Tao & Hua, Haochen & Cao, Junwei, 2021. "Coupling fault diagnosis of wind turbine gearbox based on multitask parallel convolutional neural networks with overall information," Renewable Energy, Elsevier, vol. 178(C), pages 639-650.
    2. Rubén Medina & Jean Carlo Macancela & Pablo Lucero & Diego Cabrera & René-Vinicio Sánchez & Mariela Cerrada, 2022. "Gear and bearing fault classification under different load and speed by using Poincaré plot features and SVM," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1031-1055, April.
    3. Guoping An & Qingbin Tong & Yanan Zhang & Ruifang Liu & Weili Li & Junci Cao & Yuyi Lin, 2021. "An Improved Variational Mode Decomposition and Its Application on Fault Feature Extraction of Rolling Element Bearing," Energies, MDPI, vol. 14(4), pages 1-24, February.
    4. Wei Zhang & Hong Lu & Yongquan Zhang & Zhangjie Li & Yongjing Wang & Jun Zhou & Jiangnuo Mei & Yuzhan Wei, 2022. "A Fault Diagnosis Scheme for Gearbox Based on Improved Entropy and Optimized Regularized Extreme Learning Machine," Mathematics, MDPI, vol. 10(23), pages 1-28, December.

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