Gear wear’s fault diagnosis in tail gearbox of helicopter: using K nearest neighbor recognition pattern

Document Type : Research Article

Authors

Faculty of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

Rotary systems application in aerospace, power stations, automotive industries and many others is prevalent. Maintenance of rotating system based on traditional logics is very expensive in different industries. Therefore, intelligent fault detection of engineering systems, especially mechanical ones, is important and growing issues in the industries all around the world.
In this research which done on power transmission system in tail gearbox of helicopter, an intelligent fault detection system for gear wear in tail gearbox of helicopter represented. For designing an intelligent testing system, an experimental set-up consisted of tail gearbox of helicopter with its related shafts and a real fix support condition designed and developed. Simulated fault on considered system is pinion wheel input wear, which was created in three stages، various conditions of gearbox in intact and damage state was studied .In designing this intelligent fault detection system time-domain signal analysis, discrete wavelet transform, Principal component analysis and automatic decision making techniques such as k nearest neighbor recognition method used.

Keywords

Main Subjects

References

[1] Bayoumi, A., N. Goodman, R. Shah, T. Roebuck, A.Jarvie, L. Eisner, L. Grant, and J. Keller, "Conditionbased maintenance at USC-Part III: Aircraft components mapping and testing for CBM". Proceedings of the American Helicopter Society Specialists Meeting on Condition-Based Maintenance, Huntsville, AL. 2008.
[2] Wang, W. J. and P. D. McFadden, "Application of the wavelet transform to gearbox vibration analysis".American Society of Mechanical Engineers, Petroleum Division (Publication) PD, Houston, TX, USA. 1993.
[3] Wang, W. J. and P. D. McFadden, "Application of wavelets to gearbox vibration signals for fault detection".Journal of Sound and Vibration, 192 (1996): 927-939.
[4] Saravanan, N. and K. I. Ramachandran, "Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN)". Expert Systems with Application, 37 (2010): 4168-4181.
[5] Wang, C. C., "Application of fault diagnosis in rotating machinery by using time Series analysis with neural network". Expert Systems with Application, 37 (2010):1696-1702.
[6] Li, B., M. Y. Chow, Y. Tipsuwan and J. C. Hung, "Neuralnetwork-based motor rolling bearing fault diagnosis".IEEE Transactions on Industrial Electronics, 47.5(2000): 1060-1069.
[7] Kang, Y., C. C. Wang, Y. P. Chang, C. C. Hsueh and M. C.Chang, "Certainty improvement in diagnosis of multiple faults by using versatile membership functions for fuzzy neural networks". Lecture Notes in Computer Science,3973 (2006): 370-375.
[8] Loparo, K., "Bearing fault diagnosis based on wavelet transform and fuzzy inference". Mechanical Systems and Signal Processing, 18 (2004): 1077-1095.
[9] Saravanan, N., S. Cholairajan and K. I. Ramachandran, "Vibration-based fault diagnosis of spur bevel gearbox using fuzzy technique". Expert Systems with Applications,36 (2009): 3119-3135.
[10] Yaguo, L. and J. Z. Ming, "Gear crack level identification based on weighted K nearest neighbor classification algorithm". Mechanical Systems and Signal Processing,23 (2009): 1535-1547.
[11] Demoura, E. P., C. R. Souto, A. A. Silva and M. A. S.Irmao, "Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses".Mechanical Systems and Signal Processing, 25 (2011):1765-1772.
[12] Bayoumi, A., N. Goodman, R. Shah, L. Eisner and L.Grant and J. Keller, "Conditioned-Based Maintenance at USC - Part IV: Examination and Cost-Benefit Analysis of the CBM Process". Proceedings of the American Helicopter Society Specialists Meeting on Condition-Based Maintenance, Huntsville, AL. 2008.
[13] Wuxing, L., W. T. Peter, Z. Guicai and S. Tielin,"Classification of gear faults using cumulants and the radial basis function network". Mechanical systems and signal processing, 18 (2004): 381-389.
[14] Qingbo, H., K. Fanrang and Y. Ruqiang, "Subspacebased gearbox condition monitoring by kernel principal component analysis". Mechanical Systems and Signal Processing, 21 (2007): 1755-1772.
[15] Mohammadi Takami, S. M., "Data Pre-processing and Pattern Recognition Methods", Khajeh Nasir Toosi University of Technology, 2005.
[16] Ziaei-Rad, S. and S. Nourian, "Condition Monitoring and Fault Diagnosis of Rotating Machinery",Daneshpajoohanbarin, 2008.
[17] Zakrajsek, J. J. and D. G. Lewicki," Detecting gear tooth fatigue cracks in advance of complete fracture".Tribo Test Journal, 4.4 (1998): 407-422.
[18] Zakrajsek, J. J., D. P. Townsend and H. J. Decker,"An analysis of gear fault detection methods as applied to pitting fatigue failure data". Mechanical Systems and Signal Processing, 21 (2007): 1755-1772.
Amirkabir Journal of Mechanical Engineering
Volume 49, Issue 2
August 2017
Pages 341-350

Files

Share

How to cite

Statistics