@article{50649, abstract = {{The energy turnaround and the shift towards sustainable mobility threaten the stability of European energy distribution grids due to substantially increasing load fluctuations and power demand. These challenges can critically impact assets in the distribution grid—e.g., switchgears—intensifying the need to plan, conduct, and manage the maintenance of such assets. Predictive maintenance strategies that analyze assets' current and historical condition data have been discussed as promising approaches toward that end. However, the extant research focuses on designing and improving analytical algorithms or information technology (IT) artifacts while not considering how a maintenance service is cocreated by companies with IT. This research article posits that IT and service must be aligned closely, presenting an ensemble artifact comprising a digital industrial platform and a smart service system for predictive maintenance on the distribution grid. The artifact is evaluated by conducting a willingness-to-pay analysis with asset operators, documenting their demand for condition monitoring and predictive maintenance as an integrated solution, although they still struggle with even getting the condition data of their assets. Building on these results, we formalize the knowledge in the form of design principles and implications for managing the maintenance of critical assets in the distribution grid.}}, author = {{zur Heiden, Philipp and Priefer, Jennifer and Beverungen, Daniel}}, issn = {{0018-9391}}, journal = {{IEEE Transactions on Engineering Management}}, keywords = {{Design science research, digital platform, distribution grid, IS design, predictive maintenance, smart services}}, pages = {{3641--3655}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Predictive Maintenance on the Energy Distribution Grid—Design and Evaluation of a Digital Industrial Platform in the Context of a Smart Service System}}}, doi = {{10.1109/tem.2024.3352819}}, volume = {{71}}, 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}}, } @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{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}}, }