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Bearing Fault Parameter Identification Under Varying Operating Conditions Using Vibration Signals and Evolutionary Algorithms

[+] Author Affiliations
Issam Abu-Mahfouz, Amit Banerjee

Penn State University Harrisburg, Middletown, PA

Paper No. IMECE2014-39124, pp. V04BT04A058; 7 pages
doi:10.1115/IMECE2014-39124
From:
  • ASME 2014 International Mechanical Engineering Congress and Exposition
  • Volume 4B: Dynamics, Vibration, and Control
  • Montreal, Quebec, Canada, November 14–20, 2014
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4648-3
  • Copyright © 2014 by ASME

abstract

This paper presents an effective bearing fault parameter identification scheme based on evolutionary optimization techniques. Three seeded faults in the rotating machinery supported by the test roller bearing include inner race fault, outer race fault and a single ball defect. The fault related features are extracted experimentally by processing the acquired vibration signals in both the time and frequency domain. Techniques based on the power spectral density (PSD) and wavelet transform (WT) are utilized for feature extraction. The sensitivity of the proposed method is investigated under varying operating speeds and radial bearing load. In this study, the inverse problem of parameter identification is investigated. The problem of parameter identification is recast as an optimization problem and two well known evolutionary algorithms, differential evolution (DE) and particle swarm optimization (PSO), are used to identify system parameters given a system response. For online parameter identification, differential evolution outperforms particle both in terms of adaptability and tighter convergence properties. The distinction between the two methods is not distinctively obvious on the offline parameter identification problem.

Copyright © 2014 by ASME

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