An approach to fault diagnosis for gearbox based on reconstructed energy and support vector machine

Likun Chao1, Chen Lu2, Jian Ma3

School of Reliability and Systems Engineering, Beihang University, Beijing, China

Science & Technology on Reliability & Environmental Engineering Laboratory, Beijing, China

3Corresponding author

E-mail: 1chaolikun@buaa.edu.cn, 2luchen@buaa.edu.cn, 309977@buaa.edu.cn

Received 28 September 2017; accepted 8 October 2017

DOI https://doi.org/10.21595/vp.2017.19251

 

Abstract. Normally sensors can only be mounted on the outer shell of gearbox, which induce more difficulties to diagnose gearbox such as serious noise contamination, signal coupling and transmission path effect. Taking into account the unique structural characteristics of gearbox, this paper presents a novel method of using reconstructed energy and Support Vector Machine (SVM) to diagnose various failure or fault modes of gears, shafts and bearings. First, FFT is performed to get the frequency domain information of raw vibration signals. Then, a series of reconstruction filters are designed to remove unwanted information and enhance signal components of interest, which correspond to specific fault information of various elements. Finally, SVM is utilized to classify different faults such as bent shaft, broken gear and defect bearing. The proposed approach has proved to be effective in solving gearbox faults classification of the 2009 PHM Conference Data Analysis Competition.

Keywords: reconstructed energy, support vector machine, fault classification.

References

[1]        Praveenkumara T., Saimuruganb M., Krishnakumarb P. Fault diagnosis of automobile gearbox based on machine learning techniques. Procedia Engineering, Vol. 97, 2014, p. 2092‑2098.

[2]        Faris E., David M., Cristobal R.-C. A comparative study of adaptive filters in detecting a naturally degraded bearing within a gearbox. Case Studies in Mechanical Systems and Signal Processing, Vol. 3, 2016, p. 1‑8.

[3]        Ragheb A., Ragheb M. Wind turbine gearbox technologies. 1st International Conference on Nuclear and Renewable Energy, 2010.

[4]        Forrester B. D. Advanced Vibration Analysis Techniques for Fault Detection and Diagnosis in Geared Transmission Systems. Swinburne University of Technology, 1996.

[5]        Sung C. K., Tai H. M., Chen C. W. Locating defects of a gear system by the technique of wavelet transform. Mechanism and Machine Theory, Vol. 35, Issue 8, 2000, p. 1169‑1182.

[6]        Huang N. E., Shen Z., Long S. R., et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, Vol. 454, 1998, p. 903‑995.

[7]        Baydar N., Ball A. A comparative study of acoustic and vibration signals in detection of gear failures using Wigner-Ville distribution. Mechanical Systems and Signal Processing, Vol. 15, Issue 6, 2001, p. 1091‑1107.

[8]        Wu F., Lee J. Information reconstruction method for improved clustering and diagnosis of generic gearbox signals. International Journal of Prognostics and Health Management, Vol. 2, 2011, p. 004.

[9]        Joachims T. Making Large-Scale SVM Learning Practical. Technical Report, SFB 475: Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund, 1998.

Cite this article

Chao Likun, Lu Chen, Ma Jian An approach to fault diagnosis for gearbox based on reconstructed energy and support vector machine. Vibroengineering PROCEDIA, Vol. 14, 2017, p. 136‑140.

 

© JVE International Ltd. Vibroengineering PROCEDIA. Oct 2017, Vol. 14. ISSN 2345-0533