@article{25046,
  abstract     = {{<jats:p>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.</jats:p>}},
  author       = {{Bender, Amelie}},
  issn         = {{2075-1702}},
  journal      = {{Machines}},
  keywords     = {{prognostics, RUL predictions, particle filter, uncertainty consideration, Multi-Model-Particle Filter, model-based approach, rubber-metal-elements, predictive maintenance}},
  number       = {{10}},
  title        = {{{A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions}}},
  doi          = {{10.3390/machines9100210}},
  volume       = {{9}},
  year         = {{2021}},
}