--- _id: '22724' abstract: - lang: eng text: "\r\nPredictive Maintenance as a desirable maintenance strategy in industrial applications relies on suitable condition monitoring solutions to reduce costs and risks of the monitored technical systems. In general, those solutions utilize model-based or data-driven methods to diagnose the current state or predict future states of monitored technical systems. However, both methods have their advantages and drawbacks. Combining both methods can improve uncertainty consideration and accuracy. Different combination approaches of those hybrid methods exist to exploit synergy effects. The choice of an appropriate approach depends on different requirements and the goal behind the selection of a hybrid approach.\r\n\r\n \r\n\r\nIn this work, the hybrid approach for estimating remaining useful lifetime takes potential uncertainties into account. Therefore, a data-driven estimation of new measurements is integrated within a model-based method. To consider uncertainties within the system, a differentiation between different system behavior is realized throughout diverse states of degradation.\r\n\r\nThe developed hybrid prediction approach bases on a particle filtering method combined with a machine learning method, to estimate the remaining useful lifetime of technical systems. Particle filtering as a Monte Carlo simulation technique is suitable to map and propagate uncertainties. Moreover, it is a state-of-the-art model-based method for predicting remaining useful lifetime of technical systems. To integrate uncertainties a multi-model particle filtering approach is employed. In general, resampling as a part of the particle filtering approach has the potential to lead to an accurate prediction. However, in the case where no future measurements are available, it may increase the uncertainty of the prediction. By estimating new measurements, those uncertainties are reduced within the data-driven part of the approach. Hence, both parts of the hybrid approach strive to account for and reduce uncertainties.\r\n\r\n \r\n\r\nRubber-metal-elements are employed as a use-case to evaluate the developed approach. Rubber-metal-elements, which are used to isolate vibrations in various systems, such as railways, trucks and wind turbines, show various uncertainties in their behavior and their degradation. Those uncertainties are caused by diverse inner and outer factors, such as manufacturing influences and operating conditions. By expert knowledge the influences are described, analyzed and if possible reduced. However, the remaining uncertainties are considered within the hybrid prediction method. Relative temperature is the selected measurand to describe the element’s degradation. In lifetime tests, it is measured as the difference between the element’s temperature and the ambient temperature. Thereby, the influence of the ambient temperature on the element’s temperature is taken into account. Those elements show three typical states of degradation that are identified within the temperature measurements. Depending on the particular state of degradation a new measurement is estimated within the hybrid approach to reduce potential uncertainties.\r\n\r\nFinally, the performance of the developed hybrid method is compared to a model-based method for estimating the remaining useful lifetime of the same elements. Suitable performance indices are implemented to underline the differences between the results." author: - first_name: Amelie full_name: Bender, Amelie id: '54290' last_name: Bender - first_name: Walter full_name: Sextro, Walter id: '21220' last_name: Sextro citation: ama: 'Bender A, Sextro W. Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties. In: Do P, King S, Fink Olga, eds. Proceedings of the European Conference of the PHM Society 2021. Vol 6. ; 2021. doi:https://doi.org/10.36001/phme.2021.v6i1.2843 ' apa: Bender, A., & Sextro, W. (2021). Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties. In P. Do, S. King, & Olga Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021 (Vol. 6, Issue 1). https://doi.org/10.36001/phme.2021.v6i1.2843 bibtex: '@inproceedings{Bender_Sextro_2021, title={Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties}, volume={6}, DOI={https://doi.org/10.36001/phme.2021.v6i1.2843 }, number={1}, booktitle={Proceedings of the European Conference of the PHM Society 2021}, author={Bender, Amelie and Sextro, Walter}, editor={Do, Phuc and King, Steve and Fink, Olga}, year={2021} }' chicago: Bender, Amelie, and Walter Sextro. “Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties.” In Proceedings of the European Conference of the PHM Society 2021, edited by Phuc Do, Steve King, and Olga Fink, Vol. 6, 2021. https://doi.org/10.36001/phme.2021.v6i1.2843 . ieee: 'A. Bender and W. Sextro, “Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties,” in Proceedings of the European Conference of the PHM Society 2021, 2021, vol. 6, no. 1, doi: https://doi.org/10.36001/phme.2021.v6i1.2843 .' mla: Bender, Amelie, and Walter Sextro. “Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties.” Proceedings of the European Conference of the PHM Society 2021, edited by Phuc Do et al., vol. 6, no. 1, 2021, doi:https://doi.org/10.36001/phme.2021.v6i1.2843 . short: 'A. Bender, W. Sextro, in: P. Do, S. King, Olga Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021, 2021.' conference: end_date: 2021-07-02 name: 6th European Conference of Prognostics and Health Management start_date: 2021-06-28 date_created: 2021-07-14T06:29:08Z date_updated: 2023-09-22T07:19:48Z department: - _id: '151' doi: 'https://doi.org/10.36001/phme.2021.v6i1.2843 ' editor: - first_name: 'Phuc ' full_name: 'Do, Phuc ' last_name: Do - first_name: Steve full_name: King, Steve last_name: King - first_name: ' Olga' full_name: Fink, Olga last_name: Fink intvolume: ' 6' issue: '1' keyword: - Hybrid prediction method - Multi-model particle filtering - Uncertainty quantification - RUL estimation language: - iso: eng main_file_link: - open_access: '1' url: https://papers.phmsociety.org/index.php/phme/article/view/2843 oa: '1' publication: Proceedings of the European Conference of the PHM Society 2021 publication_identifier: unknown: - 978-1-936263-34-9 publication_status: published quality_controlled: '1' status: public title: Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties type: conference user_id: '54290' volume: 6 year: '2021' ...