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Integrated Prognostics for Component Health Management Open Access

Descriptions

Other title
Subject/Keyword
Stochastic Collocation
Uncertainty Quantification
Condition Monitoring
Crack Initiation Time
Bayesian Inference
Gear
Crack Propagation
Polynomial Chaos Expansion
Surface Wear
Shock
Remaining Useful Life
Integrated Prognostics
Time-varying Operating Conditions
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Zhao, Fuqiong
Supervisor and department
Tian, Zhigang (Mechanical Engineering)
Zeng, Yong (Concordia Institute for Information Systems Engineering, Concordia University)
Examining committee member and department
Chen, Zengtao (Mechanical Engineering)
Yang, Qingyu (Industrial and Systems Engineering, Wayne State University)
Doucette, John (Mechanical Engineering)
Zuo, Ming J. (Mechanical Engineering)
Department
Department of Mechanical Engineering
Specialization
Engineering Management
Date accepted
2015-07-07T10:43:42Z
Graduation date
2015-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
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.
Language
English
DOI
doi:10.7939/R37P8TR2Q
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. 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.
Citation for previous publication
Fuqiong Zhao, Zhigang Tian and Yong Zeng, “Uncertainty quantification in gear remaining useful life prediction through an integrated prognostics method,” IEEE Transactions on Reliability, vol. 62, no. 1, pp. 146-159. 2013.Fuqiong Zhao, Zhigang Tian and Yong Zeng, “A stochastic collocation approach for efficient integrated gear health prognosis,” Mechanical Systems and Signal Processing, vol. 39, pp. 372-387, 2013.Fuqiong Zhao, Zhigang Tian, Eric Bechhoefer and Yong Zeng, “An integrated prognostics method under time-varying operating conditions,” IEEE Transactions on Reliability, vol. 64, no. 2, pp. 673-686, 2015.Fuqiong Zhao and Zhigang Tian, “Gear integrated prognosis considering crack initiation time uncertainty”. In Proceedings of the 20th ISSAT International Conference on Reliability and Quality in Design, Seattle, USA, August, 2014.Fuqiong Zhao and Zhigang Tian, “Gear remaining useful life prediction using generalized polynomial chaos collocation method”. In Proceedings of the 18th ISSAT International Conference on Reliability and Quality in Design, Boston, USA, July, 2012.Fuqiong Zhao and Zhigang Tian. ‘’Crack propagation simulation in spur gear tooth root using ANSYS’’. In Proceedings of the 17th ISSAT International Conference on Reliability and Quality in Design, Vancouver, Canada, August, 2011.

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