@inproceedings{55336, abstract = {{Predicting the remaining useful life of technical systems has gained significant attention in recent years due to increasing demands for extending the lifespan of degrading system components. Therefore, already used systems are retrofitted by integrating sensors to monitor their performance and functionality, enabling accurate diagnosis of their condition and prediction of their remaining useful life. One of the main challenges in this field is identified in the missing data from the time where the retrofitted system has already run but without being monitored by sensors. In this paper, a novel approach for the combined diagnostics and prognostics of retrofitted systems is proposed. The methodology aims to provide an accurate diagnosis of the system’s health state and estimation of the remaining useful life by a combination of a machine learning and expert knowledge. To evaluate the effectiveness of the proposed methodology, a case study involving a retrofitted system in an industrial setting is selected and applied. It is demonstrated that the approach effectively diagnose the current system’s health state and accurately predict its remaining useful life, thereby enabling predictive maintenance and decision-making. Overall, our research contributes to advancing the field of condition monitoring for retrofitted systems by providing a comprehensive methodology that addresses the challenge of missing data.}}, author = {{Bender, Amelie and Aimiyekagbon, Osarenren Kennedy and Sextro, Walter}}, booktitle = {{Proceedings of the 2024 Prognostics and System Health Management Conference (PHM)}}, isbn = {{979-8-3503-6058-5}}, keywords = {{retrofit, diagnosis, prognostics, RUL prediction, missing data, ball bearings}}, location = {{Stockholm, Schweden}}, publisher = {{IEEE Computer Society}}, title = {{{Diagnostics and Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System Health Assessment}}}, doi = {{10.1109/PHM61473.2024.00038}}, year = {{2024}}, } @inproceedings{27652, abstract = {{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. Dieser 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. }}, author = {{Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}}, booktitle = {{VDI-Berichte 2391}}, isbn = {{978-3-18-092391-8}}, issn = {{0083-5560 }}, keywords = {{run-to-failure, rubber-metal element, bearing prognostics, non-stationary operating conditions, varying operating conditions, feature extraction, feature selection}}, location = {{Würzburg}}, pages = {{197 -- 210}}, publisher = {{VDI Verlag GmbH}}, title = {{{Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten }}}, year = {{2021}}, } @article{25046, abstract = {{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.}}, author = {{Bender, Amelie}}, issn = {{2075-1702}}, journal = {{Machines}}, keywords = {{prognostics, RUL predictions, particle filter, uncertainty consideration, Multi-Model-Particle Filter, model-based approach, rubber-metal-elements, predictive maintenance}}, number = {{10}}, title = {{{A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions}}}, doi = {{10.3390/machines9100210}}, volume = {{9}}, year = {{2021}}, } @inproceedings{13460, abstract = {{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. }}, author = {{Bender, Amelie and Schinke, Lennart and Sextro, Walter}}, booktitle = {{Proceedings of the 29th European Safety and Reliability Conference (ESREL2019)}}, editor = {{Beer, Michael and Zio, Enrico}}, isbn = {{978-981-11-2724-3}}, keywords = {{RUL prediction, adaptive threshold, prognostics, condition monitoring}}, location = {{Hannover}}, number = {{29}}, pages = {{1262--1269}}, title = {{{Remaining useful lifetime prediction based on adaptive failure thresholds}}}, year = {{2019}}, } @inproceedings{9947, abstract = {{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.}}, author = {{Kimotho, James Kuria and Sextro, Walter}}, booktitle = {{Annual Conference of the Prognostics and Health Management Society 2015}}, keywords = {{ensemble methods, combined prognostics, data fusion}}, title = {{{Comparison and ensemble of temperature-based and vibration-based methods for machinery prognostics}}}, volume = {{6}}, year = {{2015}}, } @inproceedings{9879, abstract = {{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.}}, author = {{Kimotho, James Kuria and Meyer, Tobias and Sextro, Walter}}, booktitle = {{Prognostics and Health Management (PHM), 2014 IEEE Conference on}}, keywords = {{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}}, pages = {{1--6}}, title = {{{PEM fuel cell prognostics using particle filter with model parameter adaptation}}}, doi = {{10.1109/ICPHM.2014.7036406}}, year = {{2014}}, }