Analysis of distributed faults in inner and outer race of bearing via Park vector analysis method
- 119 Downloads
The Park’s transformation technique for diagnosing and statistically analyzing a variety of bearing faults is introduced in this paper. The currently used stator current analysis and instantaneous power analysis methods are not capable of diagnosing bearing distributed faults, because the defect frequency model is not available for this kind of faults. Notably, this paper has been aimed at developing a system for the non-invasive condition monitoring of bearing distributed defects on the basis of the Park vector analysis. It is also aimed at statistically evaluating the ability of this developed system to not only analyze but also segregate the localized and distributed faults in the bearings. The theoretical as well as experimental work that has been carried out demonstrates that the proposed technique can not only diagnose both types of bearing faults, but also classify them. The effectiveness of the proposed technique has been confirmed by the results obtained from real hardware implementation.
KeywordsBearing Localized faults Distributed faults Condition monitoring Intelligent diagnostics
The authors acknowledge the support from Universiti Teknologi PETRONAS for the award of Universiti Research Innovation Fund (URIF-0153-B87) and Ministry of Higher Education (MOHE) Malaysia for the award of the Prototype Research Grant Scheme (PRGS).
Compliance with ethical standards
Conflict of interest
The authors have no conflict of interest.
- 2.Irfan M, Saad N, Ibrahim R, Asirvadam VS (2013) An intelligent diagnostic condition monitoring system for ac motors via instantaneous power analysis. Int Rev Electr Eng 8(2):664–672Google Scholar
- 3.Irfan M, Saad N, Ibrahim R, Asirvadam VS (2013) An intelligent diagnostic system for condition monitoring of ac motors. In: The 8th IEEE conference on industrial electronics and applications, Melbourne, AustraliaGoogle Scholar
- 8.Irfan M, Saad N, Ibrahim R, Asirvadam VS, Hung NT (2014) Analysis of bearing outer race defects in induction motor. In: The 5th IEEE international conference on intelligent and systems (ICIAS), Kuala Lumpur, MalaysiaGoogle Scholar
- 12.Irfan M, Saad N, Ibrahim R, Asirvadam VS, Hung NT (2014) A non-invasive fault diagnosis system for induction motors in noisy environment. In: IEEE international conference on power and energy (PECon), Kuching, Malaysia, pp 271–276Google Scholar
- 20.Hurtado ZYM, Tello CP, Sarduy JG (2014) A review on detection and fault diagnosis in induction machines. Publicaciones en Ciencias y Tecnologa 8(01)Google Scholar
- 21.Mehala N (2010) Condition monitoring and fault diagnosis of induction motor using motor current signature analysis PhD Thesis, National Institute of Technology Kurukshetra, IndiaGoogle Scholar
- 23.Shah DS, Patel VN (2014) A review of dynamic modeling and fault identifications methods for rolling element bearing. In: 2nd International conference on innovations in automation and mechatronics engineering, ICIAMEGoogle Scholar
- 24.Dolenc B, Boškoski P, Juričić Đ (2015) Distributed bearing fault diagnosis based on vibration analysis. Mech Syst Signal Process 66:521–532Google Scholar
- 25.Dalvand F, Keshavarzi M, Kalantar A, Cheraghdar A (2015) Detection of generalized-roughness bearing fault using statistical-time indices of instantaneous frequency of motor voltage space vector. In: 23rd Iranian conference on electrical engineering (ICEE)Google Scholar
- 26.Salem SB, Touti W, Bacha K, Chaari A (2013) Induction motor mechanical fault identification using park vector approach. In: International conference on electrical engineering and software applications (ICEESA)Google Scholar
- 29.Irfan M, Saad N, Ibrahim R, Asirvadam VS (2015) An approach to diagnose inner race surface roughness faults in bearings of induction motors. In: IEEE International Conference on Signal and Image Analysis (ICSIPA), Kuala Lumpur, MalaysiaGoogle Scholar
- 30.Irfan M, Saad N, Ibrahim R, Asirvadam VS (2016) An online fault diagnosis system for induction motors via instantaneous power analysis. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=3Ovr9eQAAAAJ&citation_for_view=3Ovr9eQAAAAJ:Tyk-4Ss8FVUC. Tribol Trans. doi: 10.1080/10402004.2016.1190043