@inproceedings{64826, author = {{Kelber, Max and Brück, Steffen and Bhardwaj, Nishant and Aimiyekagbon, Osarenren Kennedy and Naumann, Rolf and Sextro, Walter}}, booktitle = {{Tagungsband Rad-Schiene-Tagung 2026}}, isbn = {{978-3-96892-332-1}}, location = {{Dresden}}, pages = {{206 – 208}}, publisher = {{DVV Media Group GmbH - Eurailpress}}, title = {{{Methodik zur Untersuchung der Fahrwerksparameter von Schienenfahrzeugen auf Basis optischer Schwingungsmessungen an einer ortsfesten Messstelle}}}, year = {{2026}}, } @inproceedings{64787, abstract = {{This study proposes a fault diagnostics methodology that addresses the challenges posed by highly imbalanced datasets typical of railway applications, where faulty conditions constitute the minority class. Fault diagnostics is performed from the component level upward, considering each sensor’s proximity to its respective critical component. Advanced signal analysis, feature engineering, and automated data-driven model generation techniques were explored to achieve comprehensive diagnostics, such that the model development process accounts for variations in the operating conditions and differing levels of information availability. The proposed methodology is evaluated on datasets from the MONOCAB, for scenarios with limited faulty instances and on the Beijing 2024 IEEE PHM Conference data challenge, which focused on fault diagnostics of railway systems under various fault modes and operating conditions.}}, author = {{Aimiyekagbon, Osarenren Kennedy and Löwen, Alexander and Hanselle, Raphael and Rief, Thomas and Beck, Maximilian and Sextro, Walter}}, booktitle = {{PHM Society Asia-Pacific Conference}}, keywords = {{MONOCAB, Beijing Data Challenge, Diagnostics of railway systems}}, title = {{{Multilevel fault diagnostics for railway applications using limited historical data}}}, doi = {{10.36001/phmap.2025.v5i1.4449}}, volume = {{5}}, year = {{2025}}, } @inproceedings{63193, abstract = {{The integration of data-driven models and specifically machine learning for conditon monitoring and predictive maintenance into companies, especially small and medium-sized enterprises, offers significant opportunities in reducing costs, operating more sustainably, and maintaining long-term competitiveness. However, many small and medium-sized enterprises lack the necessary resources and expertise to derive knowledge from data and integrate their own machine learning based solutions. To address this challenge, a framework is presented that enables the automated generation of data-driven models with a particular focus on condition monitoring and predictive maintenance, but applicable to other use cases as well. Using a dataset from the 2022 data challenge of the prognostics and health management society, it is demonstrated that the framework can generate high-performing models, achieving F1-scores up to 0.998, exemplarily for a classification task.}}, author = {{Löwen, Alexander and Quirin, Dennis and Hesse, Marc and Aimiyekagbon, Osarenren Kennedy and Sextro, Walter}}, booktitle = {{2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA)}}, location = {{Porto}}, publisher = {{IEEE}}, title = {{{Facilitating the Automated Generation of Data-Driven Models for the Diagnostics and Prognostics of Technical Systems}}}, doi = {{10.1109/etfa65518.2025.11205799}}, year = {{2025}}, } @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{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}}, } @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{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}}, } @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}}, }