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Condition Monitoring and Fault Diagnosis of Rolling Element Bearing in Phase Space

Karimi, Majid | 2012

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 43831 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Behzad, Mehdi
  7. Abstract:
  8. The failure in rolling element bearings as an important part of rotary machine can lead to machine breakdown.Consequently, bearings can reduce reliability of these components and therefore a maintenance strategy is essential to keep reliability in a desired level. The first and primitive strategy is called breakdown or unplanned maintenance. The next is preventive or planned maintenance. In this scheme, maintenance is done after a specified time period irrespective of machine health status. Out of place maintenance is the disadvantage of preventive maintenance. The most effective generation of maintenance strategy is condition based maintenance (CBM) which suggests maintenance decision based on the collected information through condition monitoring. Two elements of CBM are diagnosis and prognosis. Fault detection, isolation and identification are related to the diagnosis field, and fault forecasting before happening is the subject of prognosis
    The S-statistic based on the comparison of two RA’s in the phase space was applied to diagnose different types of faults in bearings (such as inner, ball and outer faults). To achieve the aim, measured signals of a normal bearing and several faulty bearings were decomposed to sub-signals by wavelet packet so that frequency of each defect is located in a unique packet. Result of the comparison between RA’s of normal and faulty sub-signals using the S-statistic indicate that the method can be applied for fault diagnosis
  9. Keywords:
  10. Bearing ; Diagnosis ; Wavelet Transform ; Reconstructed Phase ; S-Statistic

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