Prognosis of Component Degradation Under Uncertainty: A Method for Early Stage Design of a Complex Engineering System
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Lienhard_Prognosis of.pdf
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Author(s)
Yu, Bo Yang
Honda, Tomonori
Zak, Gina M.
Mitsos, Alexander
Mistry, Karan Hemant
Zubair, Syed
Sharqawy, Mostafa H.
Antar, Mohamed Abdelkerim
Yang, Maria
Lienhard, John H
Date Issued
July 2012
Journal
Proceedings of the ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis: Volume 3: Advanced Composite Materials and Processing; Robotics; Information Management and PLM; Design Engineering
Publisher
American Society of Mechanical Engineers
Citation
Yu, Bo Yang, Tomonori Honda, Gina M. Zak, Alexander Mitsos, John Lienhard, Karan Mistry, Syed Zubair, Mostafa H. Sharqawy, Mohamed Antar, and Maria C. Yang. “Prognosis of Component Degradation Under Uncertainty: A Method for Early Stage Design of a Complex Engineering System.” ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis: Volume 3: Advanced Composite Materials and Processing; Robotics; Information Management and PLM; Design Engineering (July 2, 2012).
Version
Original manuscript
Abstract
This paper proposes a method that dynamically improves a statistical model of system degradation by incorporating uncertainty. The method is illustrated by a case example of fouling, or degradation, in a heat exchanger in a cogeneration desalination plant. The goal of the proposed method is to select the best model from several representative condenser fouling models including linear, falling rate, and asymptotic fouling, and to validate and improve model parameters over the duration of operation. Maximum likelihood estimation (MLE) was applied to obtain a stochastic distribution of condenser fouling. Akaike’s Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were then computed at time intervals to assess the accuracy of the MLE results. The degradation model was further evaluated by estimating future prognoses and then cross-validating with real world fouling data. The results show the accuracy of a prognosis can be improved substantially by continuously updating fouling model parameters. The proposed method is a step toward facilitating prognosis of engineering systems in the early design stages by improving the prediction of future component degradation.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Engineering Systems Division
Abdul Latif Jameel Poverty Action Lab (Massachusetts Institute of Technology)
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DOI of Published Version
https://doi.org/10.1115/ESDA2012-82420
