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Integrated Prognostics for Component Health Management

  • Author / Creator
    Zhao, Fuqiong
  • Prognostics in engineering field is dedicated to predicting how long further a component or a system will perform their intended functions before failure. Prognostics is an essential building block in condition based maintenance. Accurate prediction of the component remaining useful life provides valuable information to decision making on maintenance planning, mission planning and logistics. Preventive actions based on remaining useful life prediction can dramatically avoid unscheduled downtime, reduce operational risk and cost, and improve the safety of the working environment. This thesis is devoted to developing integrated prognostics methods for the remaining useful life prediction of a specific component by integrating physics of failure and condition monitoring data. The first contribution of the thesis is that by combining physics and data effectively, the proposed method overcomes the limitations of existing prognostics approaches, which are mainly either physics based or data-driven. To account for the uncertainty in failure times of units in population, parameters are treated as random variables in physical degradation models. By noticing the uniqueness of the failure time for a specific unit, this study utilizes Bayesian inference to reduce the uncertainty in model parameters, which leads to a more accurate prediction on remaining useful life of the specific unit. This thesis also proposes an integrated prognostics method for the component operating under time-varying operating conditions. The capability to directly relate the load to the degradation rate is a key advantage of the proposed method over the existing data-driven methods when dealing with time-varying operating conditions. This is the second main contribution of this thesis. To cater to real-time applications of condition based maintenance, an efficient spectral method named polynomial chaos expansion is investigated for uncertainty quantification in prognostics. The proposed method is able to accelerate the uncertainty quantification in the integrated prognostics method and the computational efficiency is significantly improved, which is the third main contribution of this thesis. In addition, this thesis accounts for two important factors when developing integrated prognostics method: uncertainty in damage initiation time and shock in the degradation. These two factors have not been explicitly considered for prognostics purpose in the existing research. By simultaneously adjusting both the damage initiation time and the model parameters, the prediction accuracy is improved. The failure time reduction caused by the shock is accommodated by identifying a virtual damage initiation time. This work consists of the fourth main contribution. The integrated prognostics methods developed in this thesis are applied to spur gears. Two types of failure modes are considered. One is the tooth fracture due to bending stress and the other one is the surface wear due to sliding contact. Validation is conducted using a run-to-failure experiment on a planetary gearbox.

  • Subjects / Keywords
  • Graduation date
    2015-11
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R37P8TR2Q
  • 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
    Doctoral
  • Department
    • Department of Mechanical Engineering
  • Specialization
    • Engineering Management
  • Supervisor / co-supervisor and their department(s)
    • Zeng, Yong (Concordia Institute for Information Systems Engineering, Concordia University)
    • Tian, Zhigang (Mechanical Engineering)
  • Examining committee members and their departments
    • Doucette, John (Mechanical Engineering)
    • Zuo, Ming J. (Mechanical Engineering)
    • Yang, Qingyu (Industrial and Systems Engineering, Wayne State University)
    • Chen, Zengtao (Mechanical Engineering)