--- _id: '44672' abstract: - lang: eng text: With enhancing digitalization, condition monitoring is used in an increasing number of application fields across various industrial sectors. By its application, increased reliability as well as reduced risks and costs can be achieved. Based on different approaches, technical systems are monitored and measured data is analyzed to enable condition-based or predictive maintenance. To this end, machine learning approaches are usually implemented to diagnose the health states or predict the health index of the monitored system. However, these trained models are often black-box models, not intuitively explainable for a human. To overcome this shortcoming, a model-based approach based on physics is developed for piezoelectric bending actuators. Such a model enables a transparent representation of the system. Moreover, the model-based approach is extended by a parameter-estimation to account for sudden changes in behavior e. g. caused by occurring cracks. article_number: '114399' article_type: original author: - first_name: Amelie full_name: Bender, Amelie id: '54290' last_name: Bender citation: ama: 'Bender A. Model-based condition monitoring of piezoelectric bending actuators. Sensors and Actuators A: Physical. 2023;357. doi:10.1016/j.sna.2023.114399' apa: 'Bender, A. (2023). Model-based condition monitoring of piezoelectric bending actuators. Sensors and Actuators A: Physical, 357, Article 114399. https://doi.org/10.1016/j.sna.2023.114399' bibtex: '@article{Bender_2023, title={Model-based condition monitoring of piezoelectric bending actuators}, volume={357}, DOI={10.1016/j.sna.2023.114399}, number={114399}, journal={Sensors and Actuators A: Physical}, publisher={Elsevier BV}, author={Bender, Amelie}, year={2023} }' chicago: 'Bender, Amelie. “Model-Based Condition Monitoring of Piezoelectric Bending Actuators.” Sensors and Actuators A: Physical 357 (2023). https://doi.org/10.1016/j.sna.2023.114399.' ieee: 'A. Bender, “Model-based condition monitoring of piezoelectric bending actuators,” Sensors and Actuators A: Physical, vol. 357, Art. no. 114399, 2023, doi: 10.1016/j.sna.2023.114399.' mla: 'Bender, Amelie. “Model-Based Condition Monitoring of Piezoelectric Bending Actuators.” Sensors and Actuators A: Physical, vol. 357, 114399, Elsevier BV, 2023, doi:10.1016/j.sna.2023.114399.' short: 'A. Bender, Sensors and Actuators A: Physical 357 (2023).' date_created: 2023-05-09T09:49:44Z date_updated: 2023-05-09T09:53:31Z department: - _id: '151' doi: 10.1016/j.sna.2023.114399 intvolume: ' 357' keyword: - Condition Monitoring - Model-based approach Diagnostics - Varying conditions - Explainability - Piezoelectric bending actuators language: - iso: eng main_file_link: - open_access: '1' url: https://authors.elsevier.com/a/1h2WV3IC9dF7Hm oa: '1' publication: 'Sensors and Actuators A: Physical' publication_identifier: issn: - 0924-4247 publication_status: published publisher: Elsevier BV quality_controlled: '1' status: public title: Model-based condition monitoring of piezoelectric bending actuators type: journal_article user_id: '54290' volume: 357 year: '2023' ... --- _id: '25046' abstract: - lang: eng 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. article_number: '210' article_type: original author: - first_name: Amelie full_name: Bender, Amelie id: '54290' last_name: Bender citation: 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 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} }' 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.' 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. short: A. Bender, Machines 9 (2021). date_created: 2021-09-27T07:07:58Z date_updated: 2022-11-03T11:42:46Z department: - _id: '151' doi: 10.3390/machines9100210 intvolume: ' 9' issue: '10' keyword: - prognostics - RUL predictions - particle filter - uncertainty consideration - Multi-Model-Particle Filter - model-based approach - rubber-metal-elements - predictive maintenance language: - iso: eng main_file_link: - open_access: '1' url: https://www.mdpi.com/2075-1702/9/10/210 oa: '1' publication: Machines publication_identifier: issn: - 2075-1702 publication_status: published quality_controlled: '1' status: public title: A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions type: journal_article user_id: '54290' volume: 9 year: '2021' ...