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