Adaptive Approaches for Fault Detection and Diagnosis With Applications

  • Author / Creator
    Li, Gang
  • Adaptive approaches for model-based fault detection, diagnosis and control are the focus of this research. Both analytical and data-driven model-based techniques are studied along with the investigation of their practical applications. This thesis is organized in two parts. In Part I, adaptive fault estimation (FE) and fault tolerant shape control (FTSC) designs are proposed for stochastic distribution systems. First, an adaptive FE and dynamic output feedback FTSC scheme is designed for a class of nonlinear non-Gaussian stochastic systems subject to time-varying loss of control effectiveness faults, where time-varying faults, Lipschitz nonlinear property and general stochastic characteristics are taken into consideration simultaneously. Then, the FE and FTSC schemes are extended to particle size distribution (PSD) processes subject to simultaneous time-varying actuator and sensor faults. The proposed adaptive FE and FTSC schemes are validated in both simulation and application examples. In Part II, adaptive data-driven model-based fault detection and diagnosis (FDD) methods are proposed for rotating machinery. First, a novel sinusoidal synthesis based adaptive tracking (SSBAT) technique is proposed based on a data-driven rotating machinery model identified using time-series vibration data and incorporating physical constraints of rotating equipment; the SSBAT model is then used as an adaptive predictor to generate residual for FDD. Then, a minimum entropy deconvolution based sinusoidal synthesis (MEDSS) model is proposed to improve the fault diagnosis performance of the SSBAT scheme; a time-weighted-error Kalman filter is designed to estimate the MEDSS model parameters adaptively. Both methods are tested and proved to be effective through simulation examples and a practical case study for rubbing fault diagnosis in an industrial steam turbine.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Doctor of Philosophy
  • DOI
  • 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
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Electrical and Computer Engineering
  • Specialization
    • Control systems
  • Supervisor / co-supervisor and their department(s)
    • Zhao, Qing (Electrical and Computer Engineering)
  • Examining committee members and their departments
    • Chen, Tongwen (Electrical and Computer Engineering)
    • Lipsett, Michael (Mechanical Engineering)
    • Tavakoli, Mahdi (Electrical and Computer Engineering)
    • Zhao, Qing (Electrical and Computer Engineering)
    • Shi, Yang (Mechanical Engineering, University of Victoria)