Gearbox fault diagnosis using acoustic data by cepstrum method

Document Type : Original Article

Authors

Department of Agrotechnology, College of Abouraihan, University of Tehran, Tehran, Iran

Abstract

Fault diagnosis of rotary machines plays an essential role in reliability and safety of new industrial systems. Gears are considered as a vital part of the components of industrial machines, so that the defects of these components cause irreparable damages in industrial processes. Nowadays, many research workers conduct studies on the diagnosis of gear faults using data analysis. In this research, to acquire acoustic data from a sample gearbox, a system was fabricated and developed. Then, some common faults in the gearbox teeth were created artificially. In this research, cepstrum analysis method was used in order to detect the harmonics of gear mesh frequency and the family of sidebands created. In the primary investigation, the harmonics related to the gearbox shaft were identified with the cepstrum analysis method in the interval of 0 to 0.25 seconds. Then, in order to detect the faults of the gear, by analyzing in the interval of 0 to 0.0002 seconds, the faults related to the tooth were clearly visible and tracked. According to this research results by observing increase in amplitude of the first and fifth rahmonics, it is possible to detect faults such as broken and worn teeth of gears. The obtained results show the effectiveness of the presented method to diagnose the fault in the gearbox and prevent unexpected costs.

Keywords

References

[1] Q. He, F. Kong, R. Yan, Subspace-based gearbox condition monitoring by kernel principal component analysis, Mechanical Systems and Signal Processing, Vol. 21, No. 4, pp. 1755–1772, 2007. https://doi.org/10.1016/j.ymssp.2006.07.014
[2] R. Lu, M. R. Shahriar, P. Borghesani, R. B. Randall, Z. Peng, Removal of transfer function effects from transmission error measurements using cepstrum-based operational modal analysis, Mechanical Systems and Signal Processing, Vol. 165, p. 108324, 2022. https://doi.org/10.1016/j.ymssp.2021.108324
[3] J. Huang, X. Zhang, Vibration fault identification of a turbojet engine based on cepstrum analysis, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, Vol. 236, No. 10, pp. 1961–1970, 2022. https://doi.org/10.1177/09544100211047788
[4] H. Wang, Z. Li, X. He, D. Song, H. Guo, A novel acoustic emission parameter for predicting rock failure during Brazilian test based on cepstrum analysis, E3S Web of Conferences, Vol. 192, p. 01004, 2020. https://doi.org/:10.1051/E3SCONF/202019201004
[5] Y. Liu, Z. Jiang, H. Haizhou, J. Xiang, Asymmetric penalty sparse model based cepstrum analysis for bearing fault detections, Applied Acoustics, Vol. 165, p. 107288, 2020. https://doi.org/:10.1016/j.apacoust.2020.107288
[6] H. Illias, G. Altamimi, N. Mokhtar, H. Arof, Classification of multiple partial discharge sources in dielectric insulation material using Cepstrum analysis-artificial neural network, IEEJ Transactions on Electrical and Electronic Engineering, Vol. 12, No. 3, pp. 357–364, 2017. https://doi.org/10.1002/tee.22385
[7] B. Liang, S. D. Iwnicki, Y. Zhao, Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor fault diagnosis, Mechanical Systems and Signal Processing, Vol. 39, No. 1–2, pp. 342–360, 2013. https://doi.org/10.1016/j.ymssp.2013.02.016
[8] H. Z. Wu, H. K. Ge, D. H. Yang, B. Lu, L. J. Han, M. A. Wel, Research on cepstrum analysis of drill string vibration and extraction of bit source signals, Chinese Journal of Geophysics, Vol. 53, No. 4, pp. 684–692, 2010. https://doi.org/10.1175/0077JHM863.1
[9] D. Hanson, R. B. Randall, J. Antoni, D. J. Thompson, T. P. Waters, R. A. J. Ford, Cyclostationarity and the cepstrum for operational modal analysis of mimo systems—Part I: Modal parameter identification, Mechanical Systems and Signal Processing, Vol. 21, No. 6, pp. 2441–2458, 2007. https://doi.org/10.1016/j.ymssp.2006.11.008
[10] M. El. Badaoui, F. Guillet, J. Danière, New applications of the real cepstrum to gear signals, including definition of a robust fault indicator, Mechanical Systems and Signal Processing, Vol. 18, No. 5, pp. 1031–1046, 2004, https://doi.org/10.1016/j.ymssp.2004.01.005
[11] P. Bogert B, The quefrency alanysis of time series for echoes;Cepstrum, pseudo-autocovariance, cross-cepstrum and saphe cracking, Time Series Analysis, pp. 209-243, 1963. https://cir.nii.ac.jp/crid/1570009749540907648

Files

Share

How to cite

Statistics