@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     = {{<jats:p>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.</jats:p>}},
  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}},
}

@inproceedings{9949,
  abstract     = {{Intelligent mechatronic systems other the possibility to adapt system behavior to current dependability. This can be used to assure reliability by controlling system behavior to reach a pre-defined lifetime. By using such closed loop control, the margin of error of useful lifetime of an individual system is lowered. It is also possible to change the pre-defined lifetime during operation, by adapting system behavior to derate component usage. When planning maintenance actions, the remaining useful lifetime of each individual system has to be taken into account. Usually, stochastic properties of a fleet of systems are analyzed to create maintenance plans. Among these, the main factor is the probability of an individual system to last until maintenance. If condition-based maintenance is used, this is updated for each individual system using available information about its current state. By lowering the margin of error of useful lifetime, which directly corresponds to the time until maintenance, extended maintenance periods are made possible. Also using reliability-adaptive operation, a reversal of degradation driven maintenance planning is possible where a maintenance plan is setup not only according to system properties, but mainly to requirements imposed by maintenance personnel or infrastructure. Each system then adapts its behavior accordingly and fails according to the maintenance plan, making better use of maintenance personnel and system capabilities at the same time. In this contribution, the potential of maintenance plan driven system behavior adaptation is shown. A model including adaptation process and maintenance actions is simulated over full system lifetime to assess the advantages gained.}},
  author       = {{Meyer, Tobias and Kaul, Thorben and Sextro, Walter}},
  booktitle    = {{Proceedings of the 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes}},
  keywords     = {{Adaptive systems, Reliability analysis, Availability, Adaptive control, Maintenance, Self-optimizing systems, Self-optimizing control, Stochastic Petri-nets}},
  pages        = {{940--945}},
  title        = {{{Advantages of reliability-adaptive system operation for maintenance planning}}},
  doi          = {{10.1016/j.ifacol.2015.09.647}},
  year         = {{2015}},
}

@article{9808,
  abstract     = {{This study presents the methods employed by a team from the department of Mechatronics and Dynamics at the University of Paderborn, Germany for the 2013 PHM data challenge. The focus of the challenge was on maintenance action recommendation for an industrial machinery based on remote monitoring and diagnosis. Since an ensemble of data driven methods has been considered as the state of the art approach in diagnosis and prognosis, the first approach was to evaluate the performance of an ensemble of data driven methods using the parametric data as input and problems (recommended maintenance action) as the output. Due to close correlation of parametric data of different problems, this approach produced high misclassification rate. Event-based decision trees were then constructed to identify problems associated with particular events. To distinguish between problems associated with events that appeared in multiple problems, support vector machine (SVM) with parameters optimally tuned using particle swarm optimization (PSO) was employed. Parametric data was used as the input to the SVM algorithm and majority voting was employed to determine the final decision for cases with multiple events. A total of 165 SVM models were constructed. This approach improved the overall score from 21 to 48. The method was further enhanced by employing an ensemble of three data driven methods, that is, SVM, random forests (RF) and bagged trees (BT), to build the event based models. With this approach, a score of 51 was obtained . The results demonstrate that the proposed event based method can be effective in maintenance action recommendation based on events codes and parametric data acquired remotely from an industrial equipment.}},
  author       = {{Kimotho, James Kuria and Sondermann-Wölke, Chritoph and Meyer, Tobias and Sextro, Walter}},
  journal      = {{International Journal of Prognostics and Health Management}},
  keywords     = {{maintenance decision, Bagged trees, Decision trees, PSO-SVM, Random forests}},
  number       = {{2}},
  title        = {{{Application of Event Based Decision Tree and Ensemble of Data Driven Methods for Maintenance Action Recommendation}}},
  volume       = {{4}},
  year         = {{2013}},
}