@article{51518,
abstract = {{In applications of piezoelectric actuators and sensors, the dependability and particularly the reliability throughout their lifetime are vital to manufacturers and end-users and are enabled through condition-monitoring approaches. Existing approaches often utilize impedance measurements over a range of frequencies or velocity measurements and require additional equipment or sensors, such as a laser Doppler vibrometer. Furthermore, the non-negligible effects of varying operating conditions are often unconsidered. To minimize the need for additional sensors while maintaining the dependability of piezoelectric bending actuators irrespective of varying operating conditions, an online diagnostics approach is proposed. To this end, time- and frequency-domain features are extracted from monitored current signals to reflect hairline crack development in bending actuators. For validation of applicability, the presented analysis method was evaluated on piezoelectric bending actuators subjected to accelerated lifetime tests at varying voltage amplitudes and under external damping conditions. In the presence of a crack and due to a diminished stiffness, the resonance frequency decreases and the root-mean-square amplitude of the current signal simultaneously abruptly drops during the lifetime tests. Furthermore, the piezoelectric crack surfaces clapping is reflected in higher harmonics of the current signal. Thus, time-domain features and harmonics of the current signals are sufficient to diagnose hairline cracks in the actuators.}},
author = {{Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Hemsel, Tobias and Sextro, Walter}},
issn = {{2079-9292}},
journal = {{Electronics}},
keywords = {{piezoelectric transducer, self-sensing, fault detection, diagnostics, hairline crack, condition monitoring}},
number = {{3}},
publisher = {{MDPI AG}},
title = {{{Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions}}},
doi = {{10.3390/electronics13030521}},
volume = {{13}},
year = {{2024}},
}
@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{56862,
author = {{Redeker, Magnus and Quirin, Dennis and Schroeder, Rafael and Klausmann, Tobias and Löwen, Alexander and Wollbrink, Alexander and Stichweh, Heiko and Althoff, Simon and Bender, Amelie and Sextro, Walter and Hesse, Marc}},
booktitle = {{2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)}},
publisher = {{IEEE}},
title = {{{Towards a One-Stop-Shop Solution for the Application of Data-Driven Value-Adding Services in Production}}},
doi = {{10.1109/etfa61755.2024.10711095}},
volume = {{13}},
year = {{2024}},
}
@article{55568,
abstract = {{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.}},
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}},
}
@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}},
}
@article{44672,
abstract = {{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.}},
author = {{Bender, Amelie}},
issn = {{0924-4247}},
journal = {{Sensors and Actuators A: Physical}},
keywords = {{Condition Monitoring, Model-based approach Diagnostics, Varying conditions, Explainability, Piezoelectric bending actuators}},
publisher = {{Elsevier BV}},
title = {{{Model-based condition monitoring of piezoelectric bending actuators}}},
doi = {{10.1016/j.sna.2023.114399}},
volume = {{357}},
year = {{2023}},
}
@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}},
}
@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}},
}
@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{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}},
}
@phdthesis{21630,
abstract = {{Eine zustandsbasierte Instandhaltungsstrategie reduziert das Risiko eines Ausfalls eines technischen Systems bei gleichzeitig hoher Ausnutzung und planbaren Instandhaltungsmaßnahmen. Das Ziel dieser Arbeit liegt in der Entwicklung einer Zustandsüberwachung für Gummi-Metall-Elemente. Die Herausforderungen dieser Zustandsüberwachung leiten sich aus dem viskoelastischen Verhalten sowie dem komplexen Degradationsverhalten der Elemente ab. Infolge der daraus resultierenden Unsicherheiten werden die Elemente heutzutage präventiv instandgehalten. In Lebensdauerversuchen der Gummi-Metall-Elemente werden drei Messgrößen detektiert. Dabei wird mit der Temperatur eine Messgröße identifiziert, die am geeignetsten zur Beschreibung des Zustands der Elemente ist. Generell wird die Genauigkeit einer Zustandsüberwachung durch verschiedene Unsicherheiten beeinflusst. Für die Prognose der nutzbaren Restlebensdauer der Gummi-Metall-Elemente wird das Partikelfilter, eine verbreitete modellbasierte Methode zur Zustandsüberwachung technischer Systeme, weiterentwickelt, um Unsicherheiten im Verhalten und der Degradation der Elemente zu berücksichtigen. Anhand der Ergebnisse wird belegt, dass aufbauend auf dieser Zustandsüberwachung die Ausnutzung der Gummi-Metall-Elemente in realen Anwendungen durch eine präventive Instandhaltung erhöht werden kann. Damit bildet diese Arbeit die Basis für zukünftige, prädiktive Instandhaltungskonzepte für diese Elemente. Weiterhin bestätigt die Arbeit, dass eine Berücksichtigung vorliegender Unsicherheiten zu einem frühen Zeitpunkt im Entwicklungsprozess des Zustandsüberwachungssystems empfehlenswert ist.}},
author = {{Bender, Amelie}},
keywords = {{Zustandsüberwachung, Prognose der Restlebensdauer, modellbasierte Prognose, Partikelfilter, Unsicherheiten, Gummi, Verlässlichkeit, Lebensdauerversuche, Predictive Maintenance}},
publisher = {{Shaker}},
title = {{{Zustandsüberwachung zur Prognose der Restlebensdauer von Gummi-Metall-Elementen unter Berücksichtigung systembasierter Unsicherheiten}}},
doi = {{10.17619/UNIPB/1-1084}},
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{22507,
abstract = {{Several methods, including order analysis, wavelet analysis and empirical mode decomposition have been proposed and successfully employed for the health state estimation of technical systems operating under varying conditions. However, where information such as the speed of rotating machinery, component specifications or other domain-specific information is unavailable, such methods are often infeasible. Thus, this paper investigates the application of classical time-domain features, features from the medical field and novel features from the highly comparative time-series analysis (HCTSA) package, for the health state estimation of rotating machinery operating under varying conditions. Furthermore, several feature selection methods are investigated to identify features as viable health indicators for the diagnostics and prognostics of technical systems. As a case study, the presented methods are evaluated on real-world and experimentally acquired vibration data of bearings operating under varying speed. The results show that the selected features can successfully be employed as health indicators for technical systems operating under varying conditions.}},
author = {{Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}},
booktitle = {{Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021)}},
keywords = {{Wind turbine diagnostics, bearing diagnostics, non-stationary operating conditions, varying operating conditions, feature extraction, feature selection, fault detection, failure detection}},
title = {{{On the applicability of time series features as health indicators for technical systems operating under varying conditions}}},
year = {{2021}},
}
@inproceedings{27111,
abstract = {{In the industry 4.0 era, there is a growing need to transform unstructured data acquired by a multitude of sources into information and subsequently into knowledge to improve the quality of manufactured products, to boost production, for predictive maintenance, etc. Data-driven approaches, such as machine learning techniques, are typically employed to model the underlying relationship from data. However, an increase in model accuracy with state-of-the-art methods, such as deep convolutional neural networks, results in less interpretability and transparency. Due to the ease of implementation, interpretation and transparency to both domain experts and non-experts, a rule-based method is proposed in this paper, for prognostics and health management (PHM) and specifically for diagnostics. The proposed method utilizes the most relevant sensor signals acquired via feature extraction and selection techniques and expert knowledge. As a case study, the presented method is evaluated on data from a real-world quality control set-up provided by the European prognostics and health management society (PHME) at the conference’s 2021 data challenge. With the proposed method, our team took the third place, capable of successfully diagnosing different fault modes, irrespective of varying conditions.}},
author = {{Aimiyekagbon, Osarenren Kennedy and Muth, Lars and Wohlleben, Meike Claudia and 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 = {{PHME 2021, Feature Selection Classification, Feature Selection Clustering, Interpretable Model, Transparent Model, Industry 4.0, Real-World Diagnostics, Quality Control, Predictive Maintenance}},
number = {{1}},
pages = {{527--536}},
title = {{{Rule-based Diagnostics of a Production Line}}},
doi = {{10.36001/phme.2021.v6i1.3042}},
volume = {{6}},
year = {{2021}},
}
@inproceedings{17810,
abstract = {{In all fields, the significance of a reliable and accurate predictive model is almost unquantifiable. With deep domain knowledge, models derived from first principles typically outperforms other models in terms of reliability and accuracy. When it may become a cumbersome or an unachievable task to build or validate such models of complex (non-linear) systems, machine learning techniques are employed to build predictive models. However, the accuracy of such techniques is not only dependent on the hyper-parameters of the chosen algorithm, but also on the amount and quality of data. This paper investigates the application of classical time series forecasting approaches for the reliable prognostics of technical systems, where black box machine learning techniques might not successfully be employed given insufficient amount of data and where first principles models are infeasible due to lack of domain specific data. Forecasting by analogy, forecasting by analytical function fitting, an exponential smoothing forecasting method and the long short-term memory (LSTM) are evaluated and compared against the ground truth data. As a case study, the methods are applied to predict future crack lengths of riveted aluminium plates under cyclic loading. The performance of the predictive models is evaluated based on error metrics leading to a proposal of when to apply which forecasting approach.}},
author = {{Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}},
booktitle = {{PHM Society European Conference}},
keywords = {{PHM 2019, crack propagation, forecasting, unevenly spaced time series, step ahead prediction, short time series}},
number = {{1}},
title = {{{Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data}}},
volume = {{5}},
year = {{2020}},
}
@misc{9981,
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 Verformungskraft zugeordnet ist, durch die das elastisch verformbare Bauteilmaterial verformt wird. Ein solcher vorbestimmter Betriebszustand wird jeweils von einer Mess- und Auswerteeinheit erkannt und ein Messvorgang durch ein Startsignal selbsttätig gestartet, wobei mit wenigstens einem bauteilzugeordneten Beschleunigungssensor, die aktuelle Beschleunigung der Verformung oder daraus abgeleitete Kennwerte 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 EP 3 358 332 B1: Verfahren zur Bestimmung des Beginns einer verschleißbedingten Bauteil-Restnutzungsdauer eines elastisch verformbaren Bauteils, als Strukturteil und/oder Lagerteil eines Geräts.}}},
year = {{2020}},
}
@inproceedings{10255,
abstract = {{Gummi-Metall-Teile (GM-Teile) werden zur Schwingungsreduktion u. a. in Windenergieanlagen eingesetzt. Mögliche Anwendungen der Teile liegen in Wellen-, Generator- und Getriebelagerungen, Lagern für die Gondel und ihre Komponenten sowie in Drehmomentstützen. Mit dem Ziel eine prädiktive Instandhaltung zu realisieren, soll eine Zustandsüberwachung für die GM-Teile entwickelt werden. Diese Entwicklung basiert auf der Umsetzung diverser Schritte. Neben der funktionalen Betrachtung wird zwingend auch die konstruktive Integration der Sensoren in das überwachte Teil berücksichtigt. Der Schwerpunkt dieser Arbeit liegt auf der verwendeten Messgröße Temperatur, die mittels ausgewählter Sensorik detektiert wird. Dabei werden Lebensdauerversuche unter instationären Betriebsbedingungen durchgeführt, um diese Messdaten zu generieren. In der Datenauswertung werden sie hinsichtlich der Degradierung des GM-Teils analysiert und für die Ermittlung der nutzbaren Restlebensdauer verwendet. Rubber-metal-elements are used for isolation of vibrations e. g. in wind turbines. Possible applications of the elements are shaft bearings, generator bearings, gearbox bearings, bearings for the nacelle and its components and torque supports. In order to realize predictive maintenance, an accurate condition monitoring system for rubber-metal-elements should be developed. During that development different aspects have to be implemented. Additionally to the functional analysis, the constructive integration of the sensors into the monitored part is mandatory. The focus of this work is on the measured variable temperature, which is detected by means of appropriate sensors. Thereby lifetime tests are run under non-stationary operating conditions to generate temperature measurements. During data analysis, the measured data is analyzed regarding the degradation of the rubber-metal-elements and remaining useful lifetimes are estimated.}},
author = {{Bender, Amelie and Reinke, Kai and Sextro, Walter}},
booktitle = {{10. VDI-Fachtagung Schwingungen von Windenergieanlagen 2019}},
pages = {{241--248}},
title = {{{Konstruktion und Zustandsüberwachung eines Gummi-Metall-Teils mit integriertem Thermoelement}}},
volume = {{VDI-Berichte 2346}},
year = {{2019}},
}
@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}},
}