An Automated Feature Extraction Algorithm for Diagnosis of Gear Faults
- 93 Downloads
Gears are used for the transfer of mechanical power and are an important part of the electromechanical transmission system. Unexpected failure of gear could cause shutdown of the machines and proves to be expensive in terms of production loss and maintenance. Therefore, reliable condition monitoring is required to protect unexpected gear failures. It has been highlighted in the recently published literature that the gear faults appear at the specific gear frequencies in the instantaneous power spectrum of the motor. However, the amplitudes of these gear frequencies are very small and are shadowed by the environment noise. Thus, reliable diagnosis of gear faults is a challenge in real-time fault diagnosis systems. This issue has been addressed in this paper through the development of the automated spectral extraction algorithm. The theoretical investigation has been verified through the custom-designed experimental test rig.
KeywordsCondition monitoring Preventive maintenance Gear fault classification
The authors acknowledge the Najran University, Saudi Arabia, for providing funding and research facility. The authors also acknowledge the funding support by Universiti Teknologi PETRONAS, Malaysia, and Ministry of Higher Education, Malaysia, for the award of PRGS fund.
- 4.M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, M. Magzoub, An intelligent fault diagnosis of induction motors in an arbitrary noisy environment. J. Nondestr. Test. Eval. 15(5), 730–736 (2015)Google Scholar
- 10.E.G. Strangas, Response of electrical drives to gear and bearing faults-diagnosis under transient and steady state conditions, in Proceedings of Workshop on Electrical Machines Design Control and Diagnosis (WEMDCD), Invited paper, Paris, pp. 289–297 (2013)Google Scholar
- 17.S. Rajagopalan, T.G. Habetler, R.G. Harley, J.A. Restrepo, J.M. Alle, Non-stationary motor fault detection using recent quadratic time-frequency representations. Int. Conf. Rec. IEEE IAS Ann. Meet. 5, 2333–2339 (2006)Google Scholar
- 26.M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, N.M. Nor, A. Alwadie, A hardware and software integration approach for development of a non-invasive condition monitoring systems for motor-coupled gears faults diagnosis. Commun. Comput. Inf. Sci. 751, 642–655 (2017)Google Scholar
- 30.N. Saad, M. Irfan, R. Ibrahim, Condition Monitoring and Faults Diagnosis of Induction Motors: Electrical Signature Analysis (CRC Press, Routledge - Taylor & Francis Group, 2018)Google Scholar
- 32.M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, A. Alwadie, M. Aman, An assessment on the non-invasive methods for condition monitoring of induction motors, in Fault Diagnosis and Detection (InTech Publishing, 2017)Google Scholar
- 33.M. Aman Sheikh, N. Nor, T. Ibrahim, S. Tahir Bakhsh, M. Irfan, H. Binti Daud, Non-invasive methods for condition monitoring and electrical fault diagnosis of induction motors, in Fault Diagnosis and Detection (InTech Publishing, 2017)Google Scholar