@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}}, } @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{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}}, }