Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31229
Quality Control of Automotive Gearbox Based On Vibration Signal Analysis

Authors: Nilson Barbieri, Bruno Matos Martins, Gabriel de Sant'Anna Vitor Barbieri


In more complex systems, such as automotive gearbox, a rigorous treatment of the data is necessary because there are several moving parts (gears, bearings, shafts, etc.), and in this way, there are several possible sources of errors and also noise. The basic objective of this work is the detection of damage in automotive gearbox. The detection methods used are the wavelet method, the bispectrum; advanced filtering techniques (selective filtering) of vibrational signals and mathematical morphology. Gearbox vibration tests were performed (gearboxes in good condition and with defects) of a production line of a large vehicle assembler. The vibration signals are obtained using five accelerometers in different positions of the sample. The results obtained using the kurtosis, bispectrum, wavelet and mathematical morphology showed that it is possible to identify the existence of defects in automotive gearboxes.

Keywords: Wavelet, automotive gearbox, mathematical morphology, bispectrum

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1979


[1] W. Wang, “An evaluation of some emerging techniques for gear fault detection”, Structural Health Monitoring, vol. 2, pp. 225-242, 2003.
[2] M. El Morsy and G. Achtenova, “Vehicle Gearbox Fault Diagnosis Based On Cepstrum Analysis”, World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial and Mechatronics Engineering , vol. 8, No. 9, pp. 1533-1539, 2014.
[3] L. Nacib, K. M. Pekpe and S. Sakhara, “Detecting gear tooth cracks using cepstral analysis in gearbox of helicopters”, International Journal of Advances in Engineering & Technology, vol. 5, pp. 139-145, Jan. 2013.
[4] P. Mazal, L. Nohal, F. Hort and V. Koula, “Possibilities of the damage diagnostics of gearboxes and bearings with acoustic emissions method”, 18th World Conference on Nondestructive Testing, 8 p., 16-20 April 2012, Durban, South Africa
[5] S. M. Metwalley, N. Hammad and S. A. Abouel-seoud. “Vehicle gearbox fault diagnosis using noise measurements. Journal of Mechanical Engineering Research, vol. 2(6), pp. 116-125, November 2010.
[6] F. Combet and L. Gelman, “Optimal filtering of gear signals for early damage detection based on the spectral kurtosis”, Mechanical Systems and Signal Processing, vol. 23 , pp. 652–668, 2009.
[7] N. Sawalhi, R. B. Randall and H. Endo, “The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis”, Mechanical Systems and Signal Processing , vol. 21, pp. 2616–2633, 2007.
[8] L. Gao, Z. Ren, W. Tang, H. Wang and P. Chen, “Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR”, Sensors, vol. 10, pp. 4602-4621, 2010.
[9] N. Barbieri and R. Barbieri, "Study of Damage in Beams with Different Boundary Conditions", World Academy of Science, Engineering and Technology, vol. 7, pp. 115-121, 2013.
[10] F. E. H. Montero and O. C. Medina, “The application of bispectrum on diagnosis of rolling element bearings: A theoretical approach”, Mechanical Systems and Signal Processing, vol. 22, pp. 588–596, 2008.
[11] W. J. Wang and P. D. McFadden, “Application of wavelets to gearbox vibration signals for fault detection”, Journal of Sound and Vibration, vol. 192 (5), pp. 927-939, 1996.
[12] X. Fan and M. J. Zuo, “Gearbox fault detection using Hilbert and wavelet packet transform”, Mechanical Systems and Signal Processing, vol. 20, pp966–982, 2006.
[13] S. Hou, Y. Li and Z. Wang, “A resonance demodulation method based on harmonic wavelet transform for rolling bearing fault diagnosis”, Structural Health Monitoring, vol. 9(4), pp. 297–312, 2010.
[14] S. Hussain and H. A. Gabbar, “Fault diagnosis in gearbox using adaptive wavelet filtering and shock response spectrum features extraction”, Structural Health Monitoring, vol. 12(2), pp. 169–180, 2013.
[15] N. Vincenzo, Q. Giuseppe and F. Aniello, “The detection of gear noise computed by integrating the Fourier and Wavelet methods”, WSEAS Transactions on Signal Processing, vol. 4(3), pp. 60-67, March 2008.
[16] L. Zhang, J. Xu, J. Yang, D. Yang and D. Wang, “Multiscale morphology analysis and its application to fault diagnosis”, Mechanical Systems and Signal Processing, vol. 22, pp. 597–610, 2008.
[17] H. Li and De-yun Xiao, “Fault diagnosis using pattern classification based on one-dimensional adaptive rank-order morphological filter”, Journal of Process Control, vol. 22, pp. 436– 449, 2012.
[18] Z. Chen, N. Gao, W. Sun, Q. Chen, F. Yan, X. Zhang, M. Iftikhar, S. Liu and Z. Ren, "A Signal Based Triangular Structuring Element for Mathematical Morphological Analysis and Its Application in Rolling Element Bearing Fault Diagnosis", Shock and Vibration, vol. 2014, p. 1- 16.
[19] A. S. Raj and N. Murali, "Early classification of bearing faults using morphological operators and fuzzy inference", IEEE Transactions on Industrial Electronics, vol. 60(2), Feb. 2013.
[20] L.J. Han, L.J. Zhang, J.H. Yang, M. Li and J.W. Xu, "Method for EEG feature extraction based on morphological pattern spectrum". IEEE International Conference on Signal Acquisition and Processing, pp.68- 72, 2009.