TY - JOUR AB - While increasing digitalization enables multiple advantages for a reliable operation of technical systems, a remaining challenge in the context of condition monitoring is seen in suitable consideration of uncertainties affecting the monitored system. Therefore, a suitable prognostic approach to predict the remaining useful lifetime of complex technical systems is required. To handle different kinds of uncertainties, a novel Multi-Model-Particle Filtering-based prognostic approach is developed and evaluated by the use case of rubber-metal-elements. These elements are maintained preventively due to the strong influence of uncertainties on their behavior. In this paper, two measurement quantities are compared concerning their ability to establish a prediction of the remaining useful lifetime of the monitored elements and the influence of present uncertainties. Based on three performance indices, the results are evaluated. A comparison with predictions of a classical Particle Filter underlines the superiority of the developed Multi-Model-Particle Filter. Finally, the value of the developed method for enabling condition monitoring of technical systems related to uncertainties is given exemplary by a comparison between the preventive and the predictive maintenance strategy for the use case. AU - Bender, Amelie ID - 25046 IS - 10 JF - Machines KW - prognostics KW - RUL predictions KW - particle filter KW - uncertainty consideration KW - Multi-Model-Particle Filter KW - model-based approach KW - rubber-metal-elements KW - predictive maintenance SN - 2075-1702 TI - A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions VL - 9 ER -