Analysis of Bearing Surface Roughness Defects in Induction Motors

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• 作者：Muhammad Irfan ; Nordin Saad…
• 关键词：Bearing surface roughness faults ; Condition monitoring ; Intelligent diagnostics ; Machine vibration
• 刊名：Journal of Failure Analysis and Prevention
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：15
• 期：5
• 页码：730-736
• 全文大小：864 KB
• 参考文献：1.M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, An online condition monitoring system for induction motors via instantaneous power analysis. J. Mech. Sci. Technol. 29(4), 1483-492 (2015)CrossRef
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• 作者单位：Muhammad Irfan (1)
  Nordin Saad (1)
  Rosdiazli Ibrahim (1)
  Vijanth S. Asirvadam (1)
  N. T. Hung (1)
  Muawia A. Magzoub (1)
  
  1. Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, 31750, Malaysia
  
• 刊物类别：Chemistry and Materials Science
• 刊物主题：Chemistry
  Materials Science
  Tribology, Corrosion and Coatings
  Characterization and Evaluation Materials
  Mechanics
  Structural Mechanics
  Quality Control, Reliability, Safety and Risk
  
• 出版者：Springer Boston
• ISSN：1864-1245

文摘

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. Keywords Bearing surface roughness faults Condition monitoring Intelligent diagnostics Machine vibration