TY - GEN
T1 - A probabilistic model-based prognostics using meshfree modeling
AU - Endeshaw, Haileyesus B.
AU - Alemayehu, Fisseha M.
AU - Ekwaro-Osire, Stephen
AU - Dias, João Paulo
N1 - Publisher Copyright:
Copyright © 2016 by ASME.
PY - 2016
Y1 - 2016
N2 - Accurate prediction of remaining useful life (RUL) will improve reliability and reduce maintenance cost. Therefore, prognostics is essential to predict the RUL of systems and components. However, a big issue of uncertainty prevails in prognostics due to the fact that prognostics pertains to prediction of future state, which is affected by uncertainty. While various researches have been done in areas of prognostics and health management, they lack to perform RUL predictions efficiently. There is a need for an efficient comprehensive framework for quantifying uncertainty in prognostics. The research question to this study is: can meshfree modeling be used in probabilistic prognostics to efficiently predict RUL? The specific aims developed to answer the research question are (1) develop a computational framework for probabilistic prognostics of a fatigue life of a component using meshfree modeling, and (2) perform case study analyses on fatigue life of a cantilever beam. A probabilistic framework was developed that efficiently predicts the RUL of a component using a combination of the meshfree method known as local radial point interpolation method and a fatigue degradation model. Loading uncertainty is quantified and employed in the framework. The computational framework is easily customizable and computationally efficient and, hence, aids in decision making and fault mitigation. As a case study, the RUL of a cantilever beam under plane stress subjected to fatigue loadings was analyzed. Uncertainties in the RUL were quantified in terms of probability density functions, cumulative distribution functions, and 98% bounds of confidence interval. Sensitivity analysis was studied and computational efficiency of the framework was also investigated using first order reliability method and Monte Carlo method. When compared to the Monte Carlo method, first order reliability method provides reasonably good results and is found to be computationally more efficient.
AB - Accurate prediction of remaining useful life (RUL) will improve reliability and reduce maintenance cost. Therefore, prognostics is essential to predict the RUL of systems and components. However, a big issue of uncertainty prevails in prognostics due to the fact that prognostics pertains to prediction of future state, which is affected by uncertainty. While various researches have been done in areas of prognostics and health management, they lack to perform RUL predictions efficiently. There is a need for an efficient comprehensive framework for quantifying uncertainty in prognostics. The research question to this study is: can meshfree modeling be used in probabilistic prognostics to efficiently predict RUL? The specific aims developed to answer the research question are (1) develop a computational framework for probabilistic prognostics of a fatigue life of a component using meshfree modeling, and (2) perform case study analyses on fatigue life of a cantilever beam. A probabilistic framework was developed that efficiently predicts the RUL of a component using a combination of the meshfree method known as local radial point interpolation method and a fatigue degradation model. Loading uncertainty is quantified and employed in the framework. The computational framework is easily customizable and computationally efficient and, hence, aids in decision making and fault mitigation. As a case study, the RUL of a cantilever beam under plane stress subjected to fatigue loadings was analyzed. Uncertainties in the RUL were quantified in terms of probability density functions, cumulative distribution functions, and 98% bounds of confidence interval. Sensitivity analysis was studied and computational efficiency of the framework was also investigated using first order reliability method and Monte Carlo method. When compared to the Monte Carlo method, first order reliability method provides reasonably good results and is found to be computationally more efficient.
KW - Meshfree
KW - Probabilistic
KW - Prognostics
KW - Remaining useful life
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85032222411&partnerID=8YFLogxK
U2 - 10.1115/IMECE201667936
DO - 10.1115/IMECE201667936
M3 - Conference contribution
AN - SCOPUS:85032222411
T3 - ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
BT - ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
PB - American Society of Mechanical Engineers (ASME)
Y2 - 11 November 2016 through 17 November 2016
ER -