{"language":[{"iso":"eng"}],"date_updated":"2022-11-03T11:42:46Z","doi":"10.3390/machines9100210","oa":"1","department":[{"_id":"151"}],"publication_status":"published","publication_identifier":{"issn":["2075-1702"]},"title":"A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions","main_file_link":[{"url":"https://www.mdpi.com/2075-1702/9/10/210","open_access":"1"}],"year":"2021","type":"journal_article","citation":{"mla":"Bender, Amelie. “A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions.” Machines, vol. 9, no. 10, 210, 2021, doi:10.3390/machines9100210.","bibtex":"@article{Bender_2021, title={A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions}, volume={9}, DOI={10.3390/machines9100210}, number={10210}, journal={Machines}, author={Bender, Amelie}, year={2021} }","ama":"Bender A. A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions. Machines. 2021;9(10). doi:10.3390/machines9100210","apa":"Bender, A. (2021). A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions. Machines, 9(10), Article 210. https://doi.org/10.3390/machines9100210","chicago":"Bender, Amelie. “A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions.” Machines 9, no. 10 (2021). https://doi.org/10.3390/machines9100210.","ieee":"A. Bender, “A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions,” Machines, vol. 9, no. 10, Art. no. 210, 2021, doi: 10.3390/machines9100210.","short":"A. Bender, Machines 9 (2021)."},"_id":"25046","intvolume":" 9","article_number":"210","issue":"10","keyword":["prognostics","RUL predictions","particle filter","uncertainty consideration","Multi-Model-Particle Filter","model-based approach","rubber-metal-elements","predictive maintenance"],"publication":"Machines","quality_controlled":"1","author":[{"first_name":"Amelie","full_name":"Bender, Amelie","last_name":"Bender","id":"54290"}],"volume":9,"date_created":"2021-09-27T07:07:58Z","status":"public","abstract":[{"text":"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.","lang":"eng"}],"article_type":"original","user_id":"54290"}