[{"status":"public","abstract":[{"text":"Predicting the remaining useful life of technical \r\nsystems has gained significant attention in recent years due to \r\nincreasing demands for extending the lifespan of degrading system \r\ncomponents. Therefore, already used systems are retrofitted by \r\nintegrating sensors to monitor their performance and \r\nfunctionality, enabling accurate diagnosis of their condition and \r\nprediction of their remaining useful life. One of the main \r\nchallenges in this field is identified in the missing data from the \r\ntime where the retrofitted system has already run but without \r\nbeing monitored by sensors. In this paper, a novel approach for \r\nthe combined diagnostics and prognostics of retrofitted systems is \r\nproposed. The methodology aims to provide an accurate diagnosis \r\nof the system’s health state and estimation of the remaining useful \r\nlife by a combination of a machine learning and expert knowledge. \r\nTo evaluate the effectiveness of the proposed methodology, a case \r\nstudy involving a retrofitted system in an industrial setting is \r\nselected and applied. It is demonstrated that the approach \r\neffectively diagnose the current system’s health state and \r\naccurately predict its remaining useful life, thereby enabling \r\npredictive maintenance and decision-making. Overall, our \r\nresearch contributes to advancing the field of condition \r\nmonitoring for retrofitted systems by providing a comprehensive \r\nmethodology that addresses the challenge of missing data.","lang":"eng"}],"type":"conference","publication":"Proceedings of the 2024 Prognostics and System Health Management Conference (PHM)","language":[{"iso":"eng"}],"keyword":["retrofit","diagnosis","prognostics","RUL prediction","missing data","ball bearings"],"user_id":"54290","department":[{"_id":"151"}],"_id":"55336","citation":{"chicago":"Bender, Amelie, Osarenren Kennedy Aimiyekagbon, and Walter Sextro. “Diagnostics and Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System Health Assessment.” In <i>Proceedings of the 2024 Prognostics and System Health Management Conference (PHM)</i>. IEEE Computer Society, 2024. <a href=\"https://doi.org/10.1109/PHM61473.2024.00038\">https://doi.org/10.1109/PHM61473.2024.00038</a>.","ieee":"A. Bender, O. K. Aimiyekagbon, and W. Sextro, “Diagnostics and Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System Health Assessment,” presented at the 2024 Prognostics and System Health Management Conference (PHM), Stockholm, Schweden, 2024, doi: <a href=\"https://doi.org/10.1109/PHM61473.2024.00038\">10.1109/PHM61473.2024.00038</a>.","ama":"Bender A, Aimiyekagbon OK, Sextro W. Diagnostics and Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System Health Assessment. In: <i>Proceedings of the 2024 Prognostics and System Health Management Conference (PHM)</i>. IEEE Computer Society; 2024. doi:<a href=\"https://doi.org/10.1109/PHM61473.2024.00038\">10.1109/PHM61473.2024.00038</a>","short":"A. Bender, O.K. Aimiyekagbon, W. Sextro, in: Proceedings of the 2024 Prognostics and System Health Management Conference (PHM), IEEE Computer Society, 2024.","bibtex":"@inproceedings{Bender_Aimiyekagbon_Sextro_2024, title={Diagnostics and Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System Health Assessment}, DOI={<a href=\"https://doi.org/10.1109/PHM61473.2024.00038\">10.1109/PHM61473.2024.00038</a>}, booktitle={Proceedings of the 2024 Prognostics and System Health Management Conference (PHM)}, publisher={IEEE Computer Society}, author={Bender, Amelie and Aimiyekagbon, Osarenren Kennedy and Sextro, Walter}, year={2024} }","mla":"Bender, Amelie, et al. “Diagnostics and Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System Health Assessment.” <i>Proceedings of the 2024 Prognostics and System Health Management Conference (PHM)</i>, IEEE Computer Society, 2024, doi:<a href=\"https://doi.org/10.1109/PHM61473.2024.00038\">10.1109/PHM61473.2024.00038</a>.","apa":"Bender, A., Aimiyekagbon, O. K., &#38; Sextro, W. (2024). Diagnostics and Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System Health Assessment. <i>Proceedings of the 2024 Prognostics and System Health Management Conference (PHM)</i>. 2024 Prognostics and System Health Management Conference (PHM), Stockholm, Schweden. <a href=\"https://doi.org/10.1109/PHM61473.2024.00038\">https://doi.org/10.1109/PHM61473.2024.00038</a>"},"year":"2024","quality_controlled":"1","publication_identifier":{"isbn":["979-8-3503-6058-5"]},"conference":{"location":"Stockholm, Schweden","end_date":"2024-05-31","start_date":"2024-05-28","name":"2024 Prognostics and System Health Management Conference (PHM)"},"doi":"10.1109/PHM61473.2024.00038","title":"Diagnostics and Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System Health Assessment","date_created":"2024-07-22T09:27:57Z","author":[{"last_name":"Bender","full_name":"Bender, Amelie","id":"54290","first_name":"Amelie"},{"last_name":"Aimiyekagbon","id":"9557","full_name":"Aimiyekagbon, Osarenren Kennedy","first_name":"Osarenren Kennedy"},{"full_name":"Sextro, Walter","id":"21220","last_name":"Sextro","first_name":"Walter"}],"publisher":"IEEE Computer Society","date_updated":"2024-07-22T09:29:26Z"},{"abstract":[{"text":"Aufgrund der Fortschritte der Digitalisierung finden Systeme zur Zustandsüberwachung vermehrt Einsatz in der Industrie, um durch eine zustandsbasierte oder eine prädiktive Instandhaltung Vorteile, wie eine verbesserte Zuverlässigkeit und geringere Kosten zu erzielen. Dabei beruhen Zustandsüberwachungssysteme auf den folgenden Bausteinen: Sensorik, Datenvorverarbeitung, Merkmalsextraktion und -auswahl, Diagnose bzw. Prognose sowie einer Entscheidungsfindung basierend auf den Ergebnissen. Jeder dieser Bausteine erfordert individuelle Einstellungen, um ein geeignetes Zustandsüberwachungssystem für die jeweilige Anwendung zu entwickeln. Eine offene Fragestellung im Bereich der Zustandsüberwachung ergibt sich aufgrund der Unsicherheit der Zukunft, die sich in den zukünftigen Betriebs- und Umgebungsbedingungen zeigt. Diese Unsicherheit gilt es in allen Bausteinen zu berücksichtigen.\r\nDieser Beitrag konzentriert sich auf den Baustein Merkmalsextraktion und -selektion, mit dem Ziel anhand geeigneter Merkmale eine Prognose der nutzbaren Restlebensdauer mit hoher Genauigkeit realisieren zu können. Daher werden geeignete Merkmale aus dem Zeitbereich und daraus abgeleitete Zustandsindikatoren für die Restlebensdauerprognose von technischen Systemen vorgestellt. Dabei sind Zustandsindikatoren Kenngrößen zur Beobachtung des Zustands der kritischen Systemkomponenten. Anhand dreier Anwendungsbeispiele wird ihre Eignung evaluiert. Dabei werden Daten aus Lebensdauerversuchen unter instationären Betriebs- und Umgebungsbedingungen ausgewertet. Die auftretenden Unsicherheiten der Zukunft werden somit berücksichtigt. Die Beispielsysteme beruhen auf Gummi-Metall-Elementen und Wälzlagern. Aus den generierten Ergebnissen lässt sich schließen, dass die Zustandsindikatoren aus der betrachteten Zeitreihen-Toolbox auch unter unbekannten Betriebs- und Umgebungsbedingungen robust sind.\r\n","lang":"ger"},{"lang":"eng","text":"Due to the advances in digitalization, condition monitoring systems have found numerous applications in the industry due to benefits such as improved reliability and lowered costs through condition-based or predictive maintenance. Condition monitoring systems typically involve elements, such as data acquisition via suitable sensors, data preprocessing, feature extraction and selection, diagnostics, prognostics and (maintenance) decisions based on diagnosis or prognosis. For the application-specific development of a suitable condition monitoring system, each of these elements requires individual settings. Due to the uncertainty of the future, an open question arises in the condition monitoring field, which is reflected in unknown future operating and environmental conditions. This uncertainty needs consideration in all elements of a condition monitoring system.\r\nThis article focuses on feature extraction and selection, building on the hypothesis that the remaining useful life of a technical system can be predicted with high accuracy utilizing suitable features. In this article, health indicators derived from time-domain features that permit the monitoring of the health of critical system components are presented for predicting the remaining useful life of technical systems. Three distinct application examples based on rubber-metal elements and rolling-element bearings are evaluated to validate the suitability of the presented methods. Experimental data from accelerated lifetime tests conducted under non-stationary operating and environmental conditions are considered to take possible future uncertainties into account. It can be concluded from the acquired results that health indicators derived from the presented time series toolbox are robust to varying operating and environmental conditions.\r\n"}],"status":"public","type":"conference","publication":"VDI-Berichte 2391","keyword":["run-to-failure","rubber-metal element","bearing prognostics","non-stationary operating conditions","varying operating conditions","feature extraction","feature selection"],"language":[{"iso":"ger"}],"_id":"27652","user_id":"9557","department":[{"_id":"151"}],"year":"2021","place":"Düsseldorf","citation":{"apa":"Aimiyekagbon, O. K., Bender, A., &#38; Sextro, W. (2021). Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten . <i>VDI-Berichte 2391</i>, 197–210.","bibtex":"@inproceedings{Aimiyekagbon_Bender_Sextro_2021, place={Düsseldorf}, title={Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten }, booktitle={VDI-Berichte 2391}, publisher={VDI Verlag GmbH}, author={Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}, year={2021}, pages={197–210} }","short":"O.K. Aimiyekagbon, A. Bender, W. Sextro, in: VDI-Berichte 2391, VDI Verlag GmbH, Düsseldorf, 2021, pp. 197–210.","mla":"Aimiyekagbon, Osarenren Kennedy, et al. “Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten .” <i>VDI-Berichte 2391</i>, VDI Verlag GmbH, 2021, pp. 197–210.","ieee":"O. K. Aimiyekagbon, A. Bender, and W. Sextro, “Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten ,” in <i>VDI-Berichte 2391</i>, Würzburg, 2021, pp. 197–210.","chicago":"Aimiyekagbon, Osarenren Kennedy, Amelie Bender, and Walter Sextro. “Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten .” In <i>VDI-Berichte 2391</i>, 197–210. Düsseldorf: VDI Verlag GmbH, 2021.","ama":"Aimiyekagbon OK, Bender A, Sextro W. Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten . In: <i>VDI-Berichte 2391</i>. VDI Verlag GmbH; 2021:197-210."},"page":"197 - 210","publication_status":"published","publication_identifier":{"isbn":["978-3-18-092391-8"],"issn":["0083-5560 "]},"title":"Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten ","conference":{"location":"Würzburg","end_date":"2021-11-17","start_date":"2021-11-16","name":"3. VDI-Fachtagung  "},"publisher":"VDI Verlag GmbH","date_updated":"2022-01-06T06:57:43Z","author":[{"last_name":"Aimiyekagbon","id":"9557","full_name":"Aimiyekagbon, Osarenren Kennedy","first_name":"Osarenren Kennedy"},{"full_name":"Bender, Amelie","id":"54290","last_name":"Bender","first_name":"Amelie"},{"first_name":"Walter","id":"21220","full_name":"Sextro, Walter","last_name":"Sextro"}],"date_created":"2021-11-22T07:42:44Z"},{"publication":"Machines","abstract":[{"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>","lang":"eng"}],"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","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":{"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>.","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).","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>","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>.","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>.","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>"},"intvolume":"         9","author":[{"id":"54290","full_name":"Bender, Amelie","last_name":"Bender","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"},{"conference":{"end_date":"2019.09.26","location":"Hannover","name":"29th European Safety and Reliability Conference ","start_date":"2019.09.22"},"main_file_link":[{"open_access":"1","url":"http://rpsonline.com.sg/proceedings/9789811127243/html/0445.xml"}],"oa":"1","date_updated":"2023-09-22T07:31:53Z","author":[{"first_name":"Amelie","last_name":"Bender","id":"54290","full_name":"Bender, Amelie"},{"first_name":"Lennart","full_name":"Schinke, Lennart","last_name":"Schinke"},{"id":"21220","full_name":"Sextro, Walter","last_name":"Sextro","first_name":"Walter"}],"page":"1262-1269","citation":{"apa":"Bender, A., Schinke, L., &#38; Sextro, W. (2019). Remaining useful lifetime prediction based on adaptive failure thresholds. In M. Beer &#38; E. Zio (Eds.), <i>Proceedings of the 29th European Safety and Reliability Conference (ESREL2019)</i> (Issue 29, pp. 1262–1269).","bibtex":"@inproceedings{Bender_Schinke_Sextro_2019, title={Remaining useful lifetime prediction based on adaptive failure thresholds}, number={29}, booktitle={Proceedings of the 29th European Safety and Reliability Conference (ESREL2019)}, author={Bender, Amelie and Schinke, Lennart and Sextro, Walter}, editor={Beer, Michael and Zio, Enrico}, year={2019}, pages={1262–1269} }","short":"A. Bender, L. Schinke, W. Sextro, in: M. Beer, E. Zio (Eds.), Proceedings of the 29th European Safety and Reliability Conference (ESREL2019), 2019, pp. 1262–1269.","mla":"Bender, Amelie, et al. “Remaining Useful Lifetime Prediction Based on Adaptive Failure Thresholds.” <i>Proceedings of the 29th European Safety and Reliability Conference (ESREL2019)</i>, edited by Michael Beer and Enrico Zio, no. 29, 2019, pp. 1262–69.","ama":"Bender A, Schinke L, Sextro W. Remaining useful lifetime prediction based on adaptive failure thresholds. In: Beer M, Zio E, eds. <i>Proceedings of the 29th European Safety and Reliability Conference (ESREL2019)</i>. ; 2019:1262-1269.","ieee":"A. Bender, L. Schinke, and W. Sextro, “Remaining useful lifetime prediction based on adaptive failure thresholds,” in <i>Proceedings of the 29th European Safety and Reliability Conference (ESREL2019)</i>, Hannover, 2019, no. 29, pp. 1262–1269.","chicago":"Bender, Amelie, Lennart Schinke, and Walter Sextro. “Remaining Useful Lifetime Prediction Based on Adaptive Failure Thresholds.” In <i>Proceedings of the 29th European Safety and Reliability Conference (ESREL2019)</i>, edited by Michael Beer and Enrico Zio, 1262–69, 2019."},"publication_identifier":{"isbn":["978-981-11-2724-3"]},"publication_status":"published","_id":"13460","department":[{"_id":"151"}],"user_id":"54290","editor":[{"first_name":"Michael","last_name":"Beer","full_name":"Beer, Michael"},{"first_name":"Enrico","last_name":"Zio","full_name":"Zio, Enrico"}],"status":"public","type":"conference","title":"Remaining useful lifetime prediction based on adaptive failure thresholds","date_created":"2019-09-30T08:49:19Z","year":"2019","quality_controlled":"1","issue":"29","keyword":["RUL prediction","adaptive threshold","prognostics","condition monitoring"],"language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"Remaining useful lifetime (RUL) predictions as part of a condition monitoring system are focused in more and more research and industrial applications. To establish an efficient and precise estimate of the RUL of a technical product, different  uncertainties  have  to  be  handled.  To  minimize  the  uncertainties  of  the  RUL  estimation,  a  reliable and accurate prognostic approach as well as a good failure threshold are important. Regarding the failure threshold, most often  an  expert  sets  a  fixed  failure  threshold.  However,  neither  the  a  priori  known  failure  threshold  nor  a  fixedthreshold value are feasible in every application. Especially in the case of varying characteristics of the monitored system, an adaptive failure threshold is of great importance concerning the accuracy of the RUL estimation.  Rubber-metal-elements, which are used in a wide range of applications for vibration and sound isolation, are mon-itored by thermocouples to allow for lifetime predictions. Therefore, the element’s state is described by its temper-ature during its service life. Aiming to establish accurate RUL predictions of a rubber-metal-element, uncertainties due to nonlinear material characteristics and changing operational conditions have to be considered. Consequently, different temperature-based failure threshold definitions are implemented and compared within a particle filtering approach. "}],"publication":"Proceedings of the 29th European Safety and Reliability Conference (ESREL2019)"},{"language":[{"iso":"eng"}],"keyword":["ensemble methods","combined prognostics","data fusion"],"department":[{"_id":"151"}],"user_id":"55222","_id":"9947","status":"public","abstract":[{"text":"This paper presents a comparison of a number of prognostic methods with regard to algorithm complexity and performance based on prognostic metrics. This information serves as a guide for selection and design of prognostic systems for real-time condition monitoring of technical systems. The methods are evaluated on ability to estimate the remaining useful life of rolling element bearing. Run-to failure vibration and temperature data is used in the analysis. The sampled prognostic methods include wear-temperature correlation method, health state estimation using temperature measurement, a multi-model particle filter approach with model parameter adaptation utilizing temperature measurements, prognostics through health state estimation and mapping extracted features to the remaining useful life through regression approach. Although the performance of the methods utilizing the vibration measurements is much better than the methods using temperature measurements, the methods using temperature measurements are quite promising in terms of reducing the overall cost of the condition monitoring system as well as the computational time. An ensemble of the presented methods through weighted average is also introduced. The results show that the methods are able to estimate the remaining useful life within error bounds of +-15\\%, which can be further reduced to +-5\\% with the ensemble approach.","lang":"eng"}],"publication":"Annual Conference of the Prognostics and Health Management Society 2015","type":"conference","title":"Comparison and ensemble of temperature-based and vibration-based methods for machinery prognostics","volume":6,"date_created":"2019-05-27T08:24:50Z","author":[{"first_name":"James Kuria","last_name":"Kimotho","full_name":"Kimotho, James Kuria"},{"full_name":"Sextro, Walter","id":"21220","last_name":"Sextro","first_name":"Walter"}],"date_updated":"2019-05-27T08:25:44Z","intvolume":"         6","citation":{"ama":"Kimotho JK, Sextro W. Comparison and ensemble of temperature-based and vibration-based methods for machinery prognostics. In: <i>Annual Conference of the Prognostics and Health Management Society 2015</i>. Vol 6. ; 2015.","ieee":"J. K. Kimotho and W. Sextro, “Comparison and ensemble of temperature-based and vibration-based methods for machinery prognostics,” in <i>Annual Conference of the Prognostics and Health Management Society 2015</i>, 2015, vol. 6.","chicago":"Kimotho, James Kuria, and Walter Sextro. “Comparison and Ensemble of Temperature-Based and Vibration-Based Methods for Machinery Prognostics.” In <i>Annual Conference of the Prognostics and Health Management Society 2015</i>, Vol. 6, 2015.","apa":"Kimotho, J. K., &#38; Sextro, W. (2015). Comparison and ensemble of temperature-based and vibration-based methods for machinery prognostics. In <i>Annual Conference of the Prognostics and Health Management Society 2015</i> (Vol. 6).","bibtex":"@inproceedings{Kimotho_Sextro_2015, title={Comparison and ensemble of temperature-based and vibration-based methods for machinery prognostics}, volume={6}, booktitle={Annual Conference of the Prognostics and Health Management Society 2015}, author={Kimotho, James Kuria and Sextro, Walter}, year={2015} }","mla":"Kimotho, James Kuria, and Walter Sextro. “Comparison and Ensemble of Temperature-Based and Vibration-Based Methods for Machinery Prognostics.” <i>Annual Conference of the Prognostics and Health Management Society 2015</i>, vol. 6, 2015.","short":"J.K. Kimotho, W. Sextro, in: Annual Conference of the Prognostics and Health Management Society 2015, 2015."},"year":"2015"},{"page":"1-6","citation":{"apa":"Kimotho, J. K., Meyer, T., &#38; Sextro, W. (2014). PEM fuel cell prognostics using particle filter with model parameter adaptation. In <i>Prognostics and Health Management (PHM), 2014 IEEE Conference on</i> (pp. 1–6). <a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">https://doi.org/10.1109/ICPHM.2014.7036406</a>","mla":"Kimotho, James Kuria, et al. “PEM Fuel Cell Prognostics Using Particle Filter with Model Parameter Adaptation.” <i>Prognostics and Health Management (PHM), 2014 IEEE Conference On</i>, 2014, pp. 1–6, doi:<a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">10.1109/ICPHM.2014.7036406</a>.","short":"J.K. Kimotho, T. Meyer, W. Sextro, in: Prognostics and Health Management (PHM), 2014 IEEE Conference On, 2014, pp. 1–6.","bibtex":"@inproceedings{Kimotho_Meyer_Sextro_2014, title={PEM fuel cell prognostics using particle filter with model parameter adaptation}, DOI={<a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">10.1109/ICPHM.2014.7036406</a>}, booktitle={Prognostics and Health Management (PHM), 2014 IEEE Conference on}, author={Kimotho, James Kuria  and Meyer, Tobias and Sextro, Walter}, year={2014}, pages={1–6} }","chicago":"Kimotho, James Kuria , Tobias Meyer, and Walter Sextro. “PEM Fuel Cell Prognostics Using Particle Filter with Model Parameter Adaptation.” In <i>Prognostics and Health Management (PHM), 2014 IEEE Conference On</i>, 1–6, 2014. <a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">https://doi.org/10.1109/ICPHM.2014.7036406</a>.","ieee":"J. K. Kimotho, T. Meyer, and W. Sextro, “PEM fuel cell prognostics using particle filter with model parameter adaptation,” in <i>Prognostics and Health Management (PHM), 2014 IEEE Conference on</i>, 2014, pp. 1–6.","ama":"Kimotho JK, Meyer T, Sextro W. PEM fuel cell prognostics using particle filter with model parameter adaptation. In: <i>Prognostics and Health Management (PHM), 2014 IEEE Conference On</i>. ; 2014:1-6. doi:<a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">10.1109/ICPHM.2014.7036406</a>"},"year":"2014","date_created":"2019-05-20T13:11:02Z","author":[{"last_name":"Kimotho","full_name":"Kimotho, James Kuria ","first_name":"James Kuria "},{"first_name":"Tobias","last_name":"Meyer","full_name":"Meyer, Tobias"},{"last_name":"Sextro","id":"21220","full_name":"Sextro, Walter","first_name":"Walter"}],"date_updated":"2019-05-20T13:12:27Z","doi":"10.1109/ICPHM.2014.7036406","title":"PEM fuel cell prognostics using particle filter with model parameter adaptation","publication":"Prognostics and Health Management (PHM), 2014 IEEE Conference on","type":"conference","status":"public","abstract":[{"text":"Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5\\% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge.","lang":"eng"}],"department":[{"_id":"151"}],"user_id":"55222","_id":"9879","language":[{"iso":"eng"}],"keyword":["ageing","particle filtering (numerical methods)","proton exchange membrane fuel cells","remaining life assessment","PEM fuel cell prognostics","PHM","RUL predictions","accelerated degradation","adaptive particle filter algorithm","dynamic loading","model parameter adaptation","prognostics and health management","proton exchange membrane fuel cells","remaining useful life estimation","self-healing effect","Adaptation models","Data models","Degradation","Estimation","Fuel cells","Mathematical model","Prognostics and health management"]}]