@inproceedings{Todd:2018,
author = {Todd, MD and Leung, M and Corcoran, J and Cawley, P},
pages = {129--137},
title = {Fatigue prognosis using the uncertainty-quantified failure forecast method},
year = {2018}
}

Download

TY  - CPAPER
AB  - Several material failure modes such as fatigue have been noted to occur, after initiation phases, as a consequence of a positive-feedback mechanism. Positive feedback systems continually "accelerate" their underlying physics until failure, and as such, are unstable processes that result in the tendency of the rate of change in the observable data (or more generally, damage-sensitive features) to approach infinity. Models have been proposed based on this asymptotic property of positive feedback mechanisms for predicting the time to criticality, generally now known as the "failure forecast method". The typical implementation of this approach is to compute the inverse time rate-of-change in the features and linearly regress that data vs. time; inevitable uncertainties in the data, measurement process, or environmental contamination noise will corrupt the regression, leading to distributions in the parameters used to make the failure forecast. This study will look at a parametric implementation of the failure forecast method using a probability density function computed from the regression process and evaluating its predictive performance on fatigue data, considering regression window, sampling time, noise level, and predictor. A comparison is also drawn between the Failure Forecast Method and a conventional periodic inspection realization.
AU  - Todd,MD
AU  - Leung,M
AU  - Corcoran,J
AU  - Cawley,P
EP  - 137
PY  - 2018///
SP  - 129
TI  - Fatigue prognosis using the uncertainty-quantified failure forecast method
ER  - 

Download