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

• Published in: Neural computing & applications Vol. 31; no. Suppl 1; pp. 683 - 691
• Main Authors: Irfan, Muhammad, Saad, Nordin, Ibrahim, Rosdiazli, Asirvadam, Vijanth S., Alwadie, A.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.01.2019; Springer Nature B.V
• Subjects: Artificial Intelligence; Bearing; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Condition monitoring; Data Mining and Knowledge Discovery; Fault diagnosis; Image Processing and Computer Vision; Original Article; Probability and Statistics in Computer Science; Vector analysis; Condition monitoring; Distributed faults; Localized faults; Bearing; Intelligent diagnostics
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-017-3038-0

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.