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Vibration Signal-Based Fault Detection for Rotating Machines

  • Author / Creator
    McDonald, Geoffrey Lyall
  • Fault detection in rotating machinery has applications in fields such as wind turbines and helicopter transmissions. Detecting and diagnosing faults is important to maintenance planning, preventing equipment damage, and preventing failure. During this presentation, two novel signal-based methods will be presented; one based on adaptive control theory, one based on deconvolution. The adaptive control theory approach is an adaptive sum-of-sinusoid model used for one-step ahead prediction. The presented deconvolution approach is a periodic expansion of the well established Minimum Entropy Deconvolution method. Results are presented on simulated signals, acceleration data from a gearbox with seeded gear tooth faults, and bearing proximity sensor data from two 50MW back pressure steam turbine generators with suspected rotor-to-stator rubbing regions.

  • Subjects / Keywords
  • Graduation date
    2011-11
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3SK98
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Electrical and Computer Engineering
  • Supervisor / co-supervisor and their department(s)
    • Zhao, Qing (Department of Electrical and Computer Engineering)
  • Examining committee members and their departments
    • Tavakoli, Mahdi (Department of Electrical and Computer Engineering)
    • Hahn, Jin-Oh (Department of Mechanical Engineering)