TITLE:
A Possibilistic Approach for Uncertainty Representation and Propagation in Similarity-Based Prognostic Health Management Solutions
AUTHORS:
Loredana Cristaldi, Alessandro Ferrero, Simona Salicone, Giacomo Leone
KEYWORDS:
Data-Driven, Epistemic Uncertainty, Measurement Uncertainty, Future Uncertainty Prognostics and Health Management, Random Fuzzy Variable, Remaining Useful Life, Similarity
JOURNAL NAME:
Open Journal of Statistics,
Vol.10 No.6,
December
17,
2020
ABSTRACT: In this
paper, a data-driven prognostic model capable to deal with different sources of
uncertainty is proposed. The main novelty factor is the application of a
mathematical framework, namely a Random Fuzzy Variable (RFV) approach, for the
representation and propagation of the different uncertainty sources affecting Prognostic Health Management (PHM) applications:
measurement, future and model uncertainty. In this way,
it is possible to deal not only with measurement noise and model parameters
uncertainty due to the stochastic nature of the degradation process, but also
with systematic effects, such as systematic errors in the measurement process,
incomplete knowledge of the degradation process, subjective belief about model
parameters. Furthermore, the low analytical complexity of the employed
prognostic model allows to easily propagate the measurement and parameters
uncertainty into the RUL forecast, with no need of extensive Monte Carlo loops,
so that low requirements in terms of computation power are needed. The model
has been applied to two real application cases, showing high accuracy output,
resulting in a potentially effective tool for predictive maintenance in different industrial sectors.