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Analysis of distributed faults in inner and outer race of bearing via Park vector analysis method

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Abstract

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.

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Acknowledgements

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).

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Authors and Affiliations

  1. Electrical Engineering Department, Faculty of Engineering, Najran University, King Abdul Aziz Road, P.O. BOX 1988, Najran, Kingdom of Saudi Arabia

    Muhammad Irfan & A. Alwadie

  2. Department of Electrical and Electronics Engineering, Universiti Teknologi Petronas, 31750, Bandar Seri Iskandar, Perak, Malaysia

    Nordin Saad, Rosdiazli Ibrahim & Vijanth S. Asirvadam

Authors
  1. Muhammad Irfan
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  2. Nordin Saad
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  3. Rosdiazli Ibrahim
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  4. Vijanth S. Asirvadam
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  5. A. Alwadie
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Correspondence to Muhammad Irfan.

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Irfan, M., Saad, N., Ibrahim, R. et al. Analysis of distributed faults in inner and outer race of bearing via Park vector analysis method. Neural Comput & Applic 31 (Suppl 1), 683–691 (2019). https://doi.org/10.1007/s00521-017-3038-0

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  • DOI: https://doi.org/10.1007/s00521-017-3038-0

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