@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}}, } @misc{67245, abstract = {{This data set consist of experimental data collected during 17 run-to-failure experiments on ball bearings subjected to time-varying load and speed conditions. No defect was initiated in the bearings before the experiments. A detailed description file is enclosed.}}, author = {{Aimiyekagbon, Osarenren}}, keywords = {{Prognostics, Non-Stationary conditions, RUL, Run to failure data, Ball bearing, Varying conditions, Accelerated life tests, bearings}}, publisher = {{Zenodo}}, title = {{{Run-to-failure data set of ball bearings subjected to time-varying load and speed conditions}}}, doi = {{10.5281/ZENODO.10805042}}, year = {{2024}}, } @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{22724, abstract = {{ Predictive 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. In 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. The 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. Rubber-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. Finally, 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 = {{Bender, Amelie and Sextro, Walter}}, booktitle = {{Proceedings of the European Conference of the PHM Society 2021}}, editor = {{Do, Phuc and King, Steve and Fink, Olga}}, keywords = {{Hybrid prediction method, Multi-model particle filtering, Uncertainty quantification, RUL estimation}}, number = {{1}}, title = {{{Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties}}}, doi = {{https://doi.org/10.36001/phme.2021.v6i1.2843 }}, volume = {{6}}, 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{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}}, }