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.
Highlights
An example of model-based approaches, that rely on mathematical models to describe the degradation process and provide a Remaining Useful Life (RUL) prediction [1], are filtering methods, which are capable of accounting for the stochasticity of the process and measurement uncertainty
Is suitable for the representation and propagation in a unique mathematical framework of both aleatory and epistemic uncertainties, and in this work, it has allowed to effectively deal with the measurement uncertainty associated to the degradation data and the epistemic uncertainty associated to the RUL of those library units whose time of failure is not known
It is possible to introduce epistemic uncertainty about RULi: assuming that the test RUL forecast is performed at time stamp t* and that the user assumes that the maximum lifetime for i-th reference unit is equal to T*, RULi can be modeled as a rectangular Random Fuzzy Variable (RFV) ranging in [0, T* - t*]
Summary
A considerable amount of data is usually required to ensure heterogeneity in the bootstrapped replicas of the training set, and in the resulting models Another factor to take into account when dealing with prognostics, is the epistemic uncertainty introduced by the incomplete knowledge and information on the parameters used to model the degradation and failure processes. The main enhancement is the application of a unique mathematical framework, namely a Random Fuzzy Variable (RFV) approach, which allows the representation and propagation of the different aleatory and epistemic sources of uncertainty affecting Prognostic Health Management (PHM) applications: measurement uncertainty, present uncertainty, future uncertainty and model uncertainty. The low analytical complexity of the employed prognostic model allows to propagate measurement and parameters uncertainty into the RUL forecast, with no need of extensive MC loops, so that low requirements in terms of computation power are needed.
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