A Novel Scenario Reduction Method by 3D-Outputs Clustering for Condition-Based Maintenance Optimization

Condition-based maintenance (CBM) optimization involves considering inherent uncertainties and external uncertainties. Since computational complexity increases exponentially with the number of degradation uncertainties and stages, scenario reduction aims to select small set of typical scenarios which can maintain the probability distributions of outputs of possible scenarios. A novel scenario reduction method, 3D-outputs-clustering scenario reduction (3DOCS), is presented by considering the impacts of uncertainty parameters on the output performance for CBM optimization which have been overlooked. Since the output performance for CBM is much more essential than the inputs, the proposed scenario reduction method reduces degradation scenarios by K -means clustering of the multiple outputs of degradations scenarios for CBM. It minimizes the probabilistic distribution distances of outputs between original and selected scenarios. Case studies show that 3DOCS has advantages as a smaller distance of output performance of selected scenarios compared to that of initial scenarios.