@article{55568,
  abstract     = {{<jats:p>Historical condition monitoring data from technical systems can be utilized to develop data-driven models for predicting the remaining useful life (RUL) of similar systems, whereas the Health Index (HI) often is a crucial component. The development of robust and accurate models requires meaningful features that reflect the system’s degradation process, enabling an accurate prediction of the system's HI. Traditionally, the identification of those is supported by one of various feature ranking methods. In literature, feature interdependencies and their transferability across various similar systems are not sufficiently considered in feature selection, exacerbating the challenge of HI prediction posed by the scarcity of data and system diversity in real-world applications. This work addresses this gaps by demonstrating how filter-based feature selection, incorporating failure thresholds and cross correlations, enhances feature selection leading to improved HI prediction. The proposed methodology is applied to a novel dataset* obtained from run-to-failure experiments on geared motors conducted as part of this study, which presents the aforementioned challenges. It is revealed that classical feature selection, consisting of feature ranking only, leaves potential untapped, which is utilized by the proposed selection methodology. It is shown that the proposed feature selection methodology leads to the best result with a RMSE of 0.14 in predicting the HI of a constructive different gearbox, while the features, determined by classical feature selection, lead to a RMSE of 0.19 at best.</jats:p>}},
  author       = {{Löwen, Alexander and Wissbrock, Peter and Bender, Amelie and Sextro, Walter}},
  isbn         = {{978-1-936263-40-0}},
  journal      = {{PHM Society European Conference}},
  location     = {{Prague}},
  number       = {{1}},
  pages        = {{955--964}},
  publisher    = {{PHM Society}},
  title        = {{{Filter-based feature selection for prognostics incorporating cross correlations and failure thresholds}}},
  doi          = {{10.36001/phme.2024.v8i1.4075}},
  volume       = {{8}},
  year         = {{2024}},
}

@inproceedings{55631,
  abstract     = {{This paper investigates the remaining useful lifetime (RUL) estimation of bearings under dynamic, i.e., time-varying, operating conditions (OC). Unlike conventional studies that assume constant OC in bearing accelerated life tests, we introduce a dataset with time-varying OC during run-to-failure experiments, simulating real-world scenarios. We explore data-driven approaches to identify the transition point from a healthy to an unhealthy state and estimate the RUL. Additionally, we examine strategies for integrating OC information to enhance RUL estimations. These methodologies are evaluated through numerical experiments using various machine learning algorithms.}},
  author       = {{Javanmardi, Alireza and Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Kimotho, James Kuria and Sextro, Walter and Hüllermeier, Eyke}},
  booktitle    = {{PHM Society European Conference}},
  isbn         = {{978-1-936263-40-0}},
  location     = {{Prague, Czech Republic}},
  number       = {{1}},
  publisher    = {{PHM Society}},
  title        = {{{Remaining Useful Lifetime Estimation of Bearings Operating under Time-Varying Conditions}}},
  doi          = {{10.36001/phme.2024.v8i1.4101}},
  volume       = {{8}},
  year         = {{2024}},
}

@article{56113,
  abstract     = {{Abstract This study focuses on hybrid modeling approaches that combine physical and data-driven methods to create more effective dynamical system models. In particular, it examines discrepancy models, a type of hybrid model that integrates a physical system model with data-driven compensation for inaccuracies. The study applies two discrepancy modeling methods to a multibody system using discrepancies in the state vector and its time derivative, respectively. As an application example, a four-bar linkage with nonlinear damping is investigated, using a simplified conservative system as a physical model. The comparative analysis of the two methods shows that the continuous approach generally outperforms the discrete method in terms of accuracy and computational efficiency, especially for velocity prediction and prediction horizon. However, scenarios, where input signals for training and testing differ, present nuanced findings. When the continuous method is trained on complex signals (sine) and tested on simpler ones (stair), it struggles to deliver satisfactory results, exhibiting notably higher root mean square error (RMSE) values, particularly in angular velocity prediction. Conversely, training on simple signals (stair) and testing on complex ones (sine) surprisingly yields low RMSE values, indicating the continuous method’s adaptability. While the discrete method aligns more closely with expectations and performs better in certain scenarios, its results are consistently moderate, neither exceptional nor particularly poor. The study also introduces a selection framework for choosing the most suitable algorithm based on the specific characteristics of the modeling task. This framework provides guidance for researchers and practitioners in leveraging hybrid modeling effectively. Finally, the study concludes with an outlook on future research directions.}},
  author       = {{Wohlleben, Meike Claudia and Röder, Benedict and Ebel, Henrik and Muth, Lars and Sextro, Walter and Eberhard, Peter}},
  journal      = {{PAMM}},
  pages        = {{e202400027}},
  title        = {{{Hybrid modeling of multibody systems: Comparison of two discrepancy models for trajectory prediction}}},
  doi          = {{https://doi.org/10.1002/pamm.202400027}},
  year         = {{2024}},
}

@inproceedings{61756,
  author       = {{Scheidemann, Claus and Hemsel, Tobias and Sextro, Walter}},
  location     = {{Hannover, Germany}},
  title        = {{{Characteristic behavior of lead-free and lead-containing piezo ring ceramics in ultrasonic transducers}}},
  year         = {{2024}},
}

@inproceedings{51119,
  author       = {{Scheidemann, Claus and Hagedorn, Oliver Ernst Caspar and Hemsel, Tobias and Sextro, Walter}},
  location     = {{Jeju, Korea}},
  title        = {{{Experimental Investigation of Bond Formation and Wire Deformation in the Ultrasonic Wire Bonding Process}}},
  year         = {{2023}},
}

@inproceedings{51338,
  author       = {{Schütte, Jan and Sextro, Walter}},
  booktitle    = {{20. VDI-Fachtagung Reifen - Fahrwerk - Fahrbahn}},
  location     = {{Karlsruhe}},
  pages        = {{165--180}},
  publisher    = {{VDI Verlag GmbH}},
  title        = {{{Einfluss der Radhubkinematik auf den Reifenverschleiß}}},
  volume       = {{2425}},
  year         = {{2023}},
}

@inproceedings{47116,
  abstract     = {{This paper presents a comprehensive study on diagnosing a spacecraft propulsion system utilizing data provided by the Prognostics and Health Management (PHM) society, specifically obtained as part of the Asia-Pacific PHM conference’s data challenge 2023. The objective of the challenge is to identify and diagnose known faults as well as unknown anomalies in the spacecraft’s propulsion system, which is critical for ensuring the spacecraft’s proper functionality and safety. To address this challenge, the proposed method follows a systematic approach of feature extraction, feature selection, and model development. The models employed in this study are kMeans clustering and decision trees combined to ensembles, enriched with expert knowledge. With the method presented, our team was capable of reaching high accuracy in identifying anomalies as well as diagnosing faults, resulting in attaining the seventh place with a score of 93.08 %.}},
  author       = {{Aimiyekagbon, Osarenren Kennedy and Löwen, Alexander and Bender, Amelie and Muth, Lars and Sextro, Walter}},
  booktitle    = {{Proceedings of the Asia Pacific Conference of the PHM Society 2023 }},
  keywords     = {{PHM, Fault Diagnostics, Multiple Fault Modes, Expert-Informed Diagnostics, Anomaly Detection}},
  number       = {{1}},
  title        = {{{Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System}}},
  doi          = {{10.36001/phmap.2023.v4i1.3596}},
  volume       = {{4}},
  year         = {{2023}},
}

@inproceedings{46813,
  abstract     = {{Modelling of dynamic systems plays an important role in many engineering disciplines. Two different approaches are physical modelling and data‐driven modelling, both of which have their respective advantages and disadvantages. By combining these two approaches, hybrid models can be created in which the respective disadvantages are mitigated, with discrepancy models being a particular subclass. Here, the basic system behaviour is described physically, that is, in the form of differential equations. Inaccuracies resulting from insufficient modelling or numerics lead to a discrepancy between the measurements and the model, which can be compensated by a data‐driven error correction term. Since discrepancy methods still require a large amount of measurement data, this paper investigates the extent to which a single discrepancy model can be trained for a physical model with additional parameter dependencies without the need for retraining. As an example, a damped electromagnetic oscillating circuit is used. The physical model is realised by a differential equation describing the electric current, considering only inductance and capacitance; dissipation due to resistance is neglected. This creates a discrepancy between measurement and model, which is corrected by a data‐driven model. In the experiments, the inductance and the capacity are varied. It is found that the same data‐driven model can only be used if additional parametric dependencies in the data‐driven term are considered as well.}},
  author       = {{Wohlleben, Meike Claudia and Muth, Lars and Peitz, Sebastian and Sextro, Walter}},
  booktitle    = {{Proceedings in Applied Mathematics and Mechanics}},
  issn         = {{1617-7061}},
  keywords     = {{Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics}},
  publisher    = {{Wiley}},
  title        = {{{Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits}}},
  doi          = {{10.1002/pamm.202300039}},
  year         = {{2023}},
}

@inproceedings{51117,
  author       = {{Scheidemann, Claus and Hemsel, Tobias and Friesen, Olga and Claes, Leander and Sextro, Walter}},
  location     = {{Jeju, Korea}},
  title        = {{{Influence of Temperature and Pre-Stress on the Piezoelectric Material Behavior of Ring-Shaped Ceramics}}},
  year         = {{2023}},
}

@inproceedings{29934,
  abstract     = {{Tire and road wear are a major source of emissions of nonexhaust particulate matter (PM) and make up the largest share of microplastics in the environment. To reduce tire wear through numerical optimization of a vehicle's suspension system, fast simulations of the representative usage of a vehicle are needed. Therefore, this contribution evaluates if instead of a full simulation of a representative test drive, only specific driving maneuvers resulting from a clustering of the driving data can be used to predict tire wear. As a measure for tire wear, the friction work between tire and road is calculated. It is shown that enough clusters result in negligible deviations between the total friction work of the full simulation and the cluster simulations as well as between the distributions of the friction work over the tire width. The calculation time can be reduced to about 1% of the full simulation.}},
  author       = {{Muth, Lars and Noll, Christian and Sextro, Walter}},
  booktitle    = {{Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021}},
  editor       = {{Orlova, Anna and Cole, David}},
  isbn         = {{978-3-031-07304-5}},
  keywords     = {{Tire Wear, Vehicle Dynamics, Clustering, Virtual Test}},
  location     = {{Saint Petersburg, Russia}},
  publisher    = {{Springer}},
  title        = {{{Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data}}},
  doi          = {{10.1007/978-3-031-07305-2_92}},
  year         = {{2022}},
}

@techreport{52045,
  author       = {{Scheidemann, Claus and Hemsel, Tobias and Sextro, Walter}},
  publisher    = {{LibreCat University}},
  title        = {{{Modellbasierte Ermittlung optimaler Prozessparameter für neuartige Ultraschallbondverbindungen}}},
  doi          = {{10.2314/KXP:1879655276}},
  year         = {{2022}},
}

@inbook{29727,
  author       = {{Wohlleben, Meike Claudia and Bender, Amelie and Peitz, Sebastian and Sextro, Walter}},
  booktitle    = {{Machine Learning, Optimization, and Data Science}},
  isbn         = {{9783030954697}},
  issn         = {{0302-9743}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction}}},
  doi          = {{10.1007/978-3-030-95470-3_8}},
  year         = {{2022}},
}

@inproceedings{30371,
  abstract     = {{To achieve optimum bond results at ultrasonic bonding thick copper wire on sensitive components is quite challenging.
Bearing in mind that high normal force and ultrasonic power are needed for bond quality but as well increase stress and finally failure risk of the substrate, methods should be found to achieve high bond quality even at lower bond parameters. Therefore, bond experiments with different bond tool grove geometries have been conducted for copper and aluminum wire on direct copper bonded (DCB) substrates to investigate the impact of geometric parameters on bond formation and bond quality. The wire material depending impact of geometry changes on the bond formation and deformation was quantified. Additionally, a bonding parameter design of experiments (DOE) has been conducted for the reference and the most promising groove geometry. Higher shear values were achieved at reduced vertical tool displacement for most bonding parameter combinations, compared to the reference tool. This behavior allows for reducing ultrasonic power to obtain equal shear values; consequently, mechanical stresses in the interface decrease. This could potentially reduce the risk of chip damage and thus yield loss.}},
  author       = {{Hagedorn, Oliver Ernst Caspar and Broll, Marian and Kirsch, Olaf and Hemsel, Tobias and Sextro, Walter}},
  booktitle    = {{CIPS 2022 - 12th International Conference on Integrated Power Electronics Systems}},
  isbn         = {{ISBN 978-3-8007-5757-2 }},
  location     = {{Berlin}},
  pages        = {{138--143}},
  publisher    = {{VDE VERLAG GMBH}},
  title        = {{{Experimental Investigation of the Influence of different Bond Tool Grooves on the Bond Quality for Ultrasonic Thick Wire Bonding}}},
  year         = {{2022}},
}

@misc{47159,
  author       = {{Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}},
  booktitle    = {{ Condition Monitor}},
  issn         = {{0268-8050}},
  number       = {{425}},
  pages        = {{5 -- 10}},
  title        = {{{On the applicability of time series features as health indicators for technical systems operating under varying conditions}}},
  year         = {{2022}},
}

@inproceedings{34104,
  abstract     = {{ue to the constantly growing energy demand of power electronics and the need to reduce the size of electronic components like power modules for e-mobility, new challenges arise for ultrasonic wire bonding: the electrical connection must endure higher thermal and mechanical stress while the connecting partners become more sensitive or require more energy to get bonded. Past investigations have shown already that multi-dimensional ultrasonic bonding and welding yield the same or even better bond quality while reducing the load on the components. This contribution is intended to show whether multidi-mensional thick wire bonding is a promising concept to over-come the new challenges. The focus is on experimental investi-gations of different bond tool trajectories in ultrasonic wire bonding of aluminum and copper wire on DCB's and chips. The bond quality is analyzed by shear tests, microsections and, in the case of aluminum bonding, by a new machine learning method for an objective automated evaluation of the sheared area.}},
  author       = {{Scheidemann, Claus and Kirsch, Olaf and Hemsel, Tobias and Sextro, Walter}},
  booktitle    = {{2022 IEEE 9th Electronics System-Integration Technology Conference (ESTC)}},
  publisher    = {{IEEE}},
  title        = {{{Experimental Investigation of Multidimensional Ultrasonic Heavy Wire Bonding}}},
  doi          = {{10.1109/estc55720.2022.9939478}},
  year         = {{2022}},
}

@misc{9980,
  abstract     = {{Die Erfindung betrifft ein Gerät mit wenigstens einem elastisch verformbaren Bauteil als Strukturteil und/oder Lagerteil, auf das im Betriebsverlauf von wechselnden Betriebszuständen abhängige, unterschiedliche Verformungskräfte einwirken, die zu einem die Bauteilnutzungsdauer begrenzenden Bauteilverschleiß führen, und mit einer Einrichtung zur Bestimmung der Bauteilnutzungsdauer und einer verschleißbedingten Bauteil-Restnutzungsdauer. Erfindungsgemäß wird ein sich zeitversetzt wiederholender, jeweils gleicher Betriebszustand vorbestimmt, dem eine jeweils gleiche, periodisch wirkende Verformungskraft zugeordnet ist, durch die das elastisch verformbare Bauteilmaterial periodisch verformt wird, wobei durch Walkarbeit ein Energieeintrag mit einem messbaren Temperaturanstieg im Vergleich zu einer Umgebungstemperatur erfolgt und wobei der jeweilige Temperaturanstieg als Kenngröße im Verlauf einer Bauteilnutzungsdauer entsprechend einer abnehmenden Bauteilsteifigkeit größer wird. Ein solcher vorbestimmter Betriebszustand wird jeweils von einer Messund Auswerteeinheit erkannt und ein Messvorgang durch ein Startsignal selbsttätig gestartet, wobei mit wenigstens einem bauteilzugeordneten Temperatursensor, der aktuelle Temperaturanstieg im Vergleich zur Umgebungstemperatur als Kenngröße für eine aktuelle Bauteilsteifigkeit gemessen und jeweils in einer Messkurve gespeichert und verglichen wird.}},
  author       = {{Reinke, Kai and Bender, Amelie and Meyer, Tobias and Sextro, Walter and Kimotho, James Kuria}},
  pages        = {{1}},
  title        = {{{Patent DE 10 2017 000 926 B4: Gerät mit wenigstens einem elastisch verformbaren Bauteil, insbesondere einem Gummi-Metall-Lager und mit einer Einrichtung zur Feststellung des Beginns einer verschleißbedingten Bauteil-Restnutzungsdauer, sowie Verfahren zur Bestimmung der Bauteil-Restnutzungsdauer.}}},
  year         = {{2022}},
}

@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{27508,
  abstract     = {{<jats:p>To analyze the influence of suspension kinematics on tire wear, detailed simulation models are required. In this study, a non-linear, flexible multibody model of a rear axle system is built up in the simulation software MSC Adams/View. The physical model comprises the suspension kinematics, compliance, and dynamics as well as the non-linear behavior of the tire using the FTire model. FTire is chosen because it has a separate tire tread model to compute the contact pressure and friction force distribution in the tire contact patch. To build up the simulation model, a large amount of data is needed. Bushings, spring, and damper characteristics are modeled based on measurements. For the structural components (e.g., control arms), reverse engineering techniques are used. The components are 3D-scanned, reworked, and included as a modal reduced finite element (FE)-model using component mode synthesis by Craig–Bampton. Finally, the suspension model is validated by comparing the simulated kinematic and compliance characteristics to experimental results. To investigate the interaction of suspension kinematics and tire wear, straight line driving events, such as acceleration, driving with constant velocity, and deceleration, are simulated with different setups of wheel suspension kinematics. The influence of the setups on the resulting friction work between tire and road is examined, and an exemplarily calculation of tire wear based on a validated FTire tire model is carried out. The results demonstrate, on the one hand, that the chosen concept of elasto-kinematic axle leads to a relatively good match with experimental results and, on the other hand, that there are significant possibilities to reduce tire wear by adjusting the suspension kinematics.</jats:p>}},
  author       = {{Schütte, Jan and Sextro, Walter}},
  issn         = {{2624-8921}},
  journal      = {{Vehicles}},
  pages        = {{233--256}},
  title        = {{{Tire Wear Reduction Based on an Extended Multibody Rear Axle Model}}},
  doi          = {{10.3390/vehicles3020015}},
  year         = {{2021}},
}

@article{21436,
  abstract     = {{Ultrasonic wire bonding is a solid-state joining process, used in the electronics industry to form electrical connections, e.g. to connect electrical terminals within semiconductor modules. Many process parameters affect the bond strength, such like the bond normal force, ultrasonic power, wire material and bonding frequency. Today, process design, development, and optimization is most likely based on the knowledge of process engineers and is mainly performed by experimental testing. In this contribution, a newly developed simulation tool is presented, to reduce time and costs and efficiently determine optimized process parameter. Based on a co-simulation of MATLAB and ANSYS, the different physical phenomena of the wire bonding process are considered using finite element simulation for the complex plastic deformation of the wire and reduced order models for the transient dynamics of the transducer, wire, substrate and bond formation. The model parameters such as the coefficients of friction between bond tool and wire and between wire and substrate were determined for aluminium and copper wire in experiments with a test rig specially developed for the requirements of heavy wire bonding. To reduce simulation time, for the finite element simulation a restart analysis and high performance computing is utilized. Detailed analysis of the bond formation showed, that the normal pressure distribution in the contact between wire and substrate has high impact on bond formation and distribution of welded areas in the contact area.}},
  author       = {{Schemmel, Reinhard and Krieger, Viktor and Hemsel, Tobias and Sextro, Walter}},
  issn         = {{0026-2714}},
  journal      = {{Microelectronics Reliability}},
  keywords     = {{Ultrasonic heavy wire bonding, Co-simulation, ANSYS, MATLAB, Process optimization, Friction coefficient, Copper-copper, Aluminium-copper}},
  pages        = {{114077}},
  title        = {{{Co-simulation of MATLAB and ANSYS for ultrasonic wire bonding process optimization}}},
  doi          = {{https://doi.org/10.1016/j.microrel.2021.114077}},
  volume       = {{119}},
  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}},
}