Analysis of Bearing Surface Roughness Defects in Induction Motors
In this paper, a Park’s transformation method for the analysis of various bearing surface roughness defects is presented. The existing instantaneous power analysis and stator current analysis techniques are unable to diagnose bearing surface roughness defects, due to the fact that characteristics defect frequency model is not available for these types of defects. Thus, this paper proposes a Park’s transformation method which can detect surface roughness defects without requiring information of the characteristic defect frequencies. The theoretical and experimental work conducted shows that the proposed method can detect bearing outer and inner race surface roughness faults without use of any extra hardware. The results on the real hardware implementation confirm the effectiveness of the proposed approach.
KeywordsBearing surface roughness faults Condition monitoring Intelligent diagnostics Machine vibration
The authors acknowledge the support from Universiti Teknologi PETRONAS and Ministry of Higher Education (MOHE) Malaysia for the award of the Exploratory Research Grant Scheme (ERGS /1/2012/TK02/UTP/02/09).
- 2.M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, An intelligent diagnostic condition monitoring system for AC motors via instantaneous power analysis. Int. Rev. Electr. Eng. 8(2), 664–672 (2013)Google Scholar
- 3.M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, An intelligent diagnostic system for condition monitoring of AC motors, in The 8th IEEE Conference on Industrial Electronics and Applications, Melbourne, Australia, June 2013.Google Scholar
- 8.M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, N.T. Hung, Analysis of bearing outer race defects in induction motor, in The 5th IEEE International Conference on Intelligent and Systems (ICIAS), Kualalumpur, Malaysia, June 2014Google Scholar
- 12.M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, N.T. Hung, A non-invasive fault diagnosis system for induction motors in noisy environment, in IEEE International Conference on Power and Energy (PECon), Kuching, Malaysia, December 2014, pp. 271–276Google Scholar
- 19.W. Zhou, T.G. Habetler, R.G. Harley, Bearing condition monitoring methods for electric machines: a general review, in IEEE International System Diagnostics Electric Machines & Power Electronics Drives, 2007Google Scholar
- 20.S.B. Salem, W. Touti, K. Bacha, A. Chaari, Induction motor mechanical fault identification using park vector approach, in International Conference on Electrical Engineering and Software Applications (ICEESA), March 2013Google Scholar
- 21.N. Mehala, Condition monitoring and fault diagnosis of induction motor using motor current signature analysis, PhD Thesis, National Institute of Technology Kurukshetra, India, 2010Google Scholar
- 22.J. Zarei, J. Poshtan, An advanced Park’s vectors approach for bearing fault detection, in IEEE International Conference on Industrial Technology, December 15–17, 2006, pp. 1472–1479Google Scholar