Abstract
Condition-based maintenance (CBM) has been widely applied to maintenance policy. Due to the stochastic forecasting degradation, scenario reduction method has been developed to improve the efficiency of CBM. However, most existing scenario reduction methods focus mainly on the input performance of the forecasting degradation without considering the significant output performance characteristic based on the model. In order to warrant the CBM optimization precision while reducing stochastic degradation scenarios efficiently, a new scenario reduction method is formulated that the scenarios with same objective function values can be reduced to one representative scenario. As a result, the reduced scenarios by the proposed method can maintain the probability distributions of objective values, while keeping optimal thresholds close to that of initial scenarios. Finally, the method is applied to select the representative degradation scenarios for CBM optimization model by utilizing vibration-based degradation signals from a rotating machinery application. Compared to the traditional scenario reduction method, the proposed method further improves accuracy and reduction efficiency of CBM optimization.DOI: http://dx.doi.org/10.5755/j01.mech.23.5.15435
Highlights
Condition-based maintenance (CBM) is a maintenance program that recommends maintenance decisions based on the information collected through condition monitoring [1]
For CBM incorporating with stochastic degradation, the current research work can be divided into two types in accordance of the planning horizon: infinite horizon and finite horizon [8]
For CBM, the optimal decision is made based on various forecasting degradation scenarios, since the degradation evolution process is stochastic within the planning horizon
Summary
Condition-based maintenance (CBM) is a maintenance program that recommends maintenance decisions based on the information collected through condition monitoring [1]. A scenario optimal reduction technique, introduced by Dupačová et al [14], applied the Foret-Mourier distance and duality theory to compute the distance between two probability measures Another variant of the scenario optimal reduction, introduced by De Oliveira et al [13], combined with global and local (stage-wise) reduction, was to select a small set of sequences representing the stochastic process well enough. These scenario reduction methods based on input performance focus mainly on the scenario parameters, but overlooking where the uncertainties appear in the problem modelling and their impacts on the optimal decision [16].
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