Multilevel fault diagnostics for railway applications using limited historical data
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Published
Jan 13, 2026
Osarenren Kennedy Aimiyekagbon
Alexander Löwen
Raphael Hanselle
Thomas Rief
Maximilian Beck
Walter Sextro
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.
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Keywords
MONOCAB;, Beijing Data Challenge, Diagnostics of railway systems
References
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Luque, A., Carrasco, A., Martín, A., & de Las Heras, A. (2019). The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 91, 216-231.
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Bernal, E., Spiryagin, M., & Cole, C. (2018). Onboard condition monitoring sensors, systems and techniques for freight railway vehicles: a review. IEEE sensors journal, 19(1), 4-24.
Christ, M., Braun, N., Neuffer, J., & Kempa-Liehr, A. W. (2018). Time series feature extraction on basis of scalable hypothesis tests (tsfresh–a python package). Neurocomputing, 307, 72-77.
Clements, N. S. (2011). Introduction to prognostics. In tutorial, proceedings of the annual conference of the prognostics and health management society.
Ding, A., Qin, Y., Wang, B., Guo, L., Jia, L., & Cheng, X. (2024). Evolvable graph neural network for system-level incremental fault diagnosis of train transmission systems. Mechanical Systems and Signal Processing, 210, 111175.
Fulcher, B. D., & Jones, N. S. (2017). hctsa: A computational framework for automated time-series phenotyping using massive feature extraction. Cell systems, 5(5), 527-531.
Fulcher, B. D., Little, M. A., & Jones, N. S. (2013). Highly comparative time-series analysis: the empirical structure of time series and their methods. Journal of the Royal Society Interface, 10(83), 20130048.
Griese, M., Döding, P., & Schulte, T. (2023). Analysis of mechanical eigenmodes of a self-stabilizing monorail vehicle. In The iavsd international symposium on dynamics of vehicles on roads and tracks (pp. 107-116).
Griese, M., & Schulte, T. (2025). Gyroscopic effects in the structural dynamics of monorail vehicles. Vehicle System Dynamics, 1-22.
Hanselle, R., Griese, M., Rasche, R., & Schulte, T. (2023). Hil simulation of the positioning control for an automated driving monorail vehicle. In 2023 ieee 21st international conference on industrial informatics (indin) (pp. 1-6).
Head, T., Kumar, M., Nahrstaedt, H., Louppe, G., & Shcherbatyi, I. (2021, October). scikit-optimize:
Sequential model-based optimization in python. https://zenodo.org/records/5565057. Zenodo. (Last accessed: April 29, 2025)
Isermann, R. (1994). Integration of fault detection and diagnosis methods. IFAC Proceedings Volumes, 27(5), 575-590.
ISO 17359:2018(en). (2018). Condition monitoring and diagnostics of machines - General guidelines (Standard). International Organization for Standardization (ISO), Geneva, Switzerland.
Löwen, A., Quirin, D., Hesse, M., & Aimiyekagbon, O. (2025). Facilitating the automated generation of datadriven models for the diagnostics and prognostics of technical systems. In 2025 ieee 30th international conference on emerging technologies and factory automation (etfa). (accepted for publication)
Luque, A., Carrasco, A., Martín, A., & de Las Heras, A. (2019). The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 91, 216-231.
Müller, T. (2009). Integration von verlässlichkeitsanalysen und-konzepten innerhalb der entwicklungsmethodik mechatronischer systeme (Unpublished doctoral dissertation). Paderborn, Universität, Diss., 2009.
Nicholaus, I. T., Park, J. R., Jung, K., Lee, J. S., & Kang, D.-K. (2021, oct). Anomaly detection of water level using deep autoencoder. Sensors (Basel), 21(19), 6679. doi: 10.3390/s21196679
Pang, J., Dai, J., & Chen, Y. (2024). A fmea optimization method based on sm-ewm and vikor method in intuitionistic fuzzy environment for risk assessment of electromagnetic brake. In 2024 4th international conference on electrical engineering and mechatronics technology (iceemt) (pp. 54-59).
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., . . . Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Rezvanizaniani, S., Valibeigloo, M., Asghari, M., Barabady, J., & Kumar, U. (2008). Reliability centered maintenance for rolling stock: A case study in coaches’ wheel sets of passenger trains of iranian railway. In 2008 ieee international conference on industrial engineering and engineering management (pp. 516–520).
Szkoda, M., & Satora, M. (2019). The application of failure mode and effects analysis (fmea) for the risk assessment of changes in the maintenance system of railway vehicles. Technical Transactions, 2019(Year 2019 (116)), 159-172.
Too, J., Abdullah, A. R., Mohd Saad, N., & Tee, W. (2019). Emg feature selection and classification using a pbest-guide binary particle swarm optimization. Computation, 7(1), 12.
Too, J., Abdullah, A. R., & Saad, N. M. (2019). Classification of hand movements based on discrete wavelet transform and enhanced feature extraction. International Journal of Advanced Computer Science and Applications, 10(6), 83-89.
Van Der Donckt, J., Van Der Donckt, J., Deprost, E., & Van Hoecke, S. (2022). tsflex: Flexible time series processing & feature extraction. SoftwareX, 17, 100971.
Wei, Y., Jang-Jaccard, J., Xu, W., Sabrina, F., Camtepe, S., & Boulic, M. (2023). Lstm-autoencoder-based anomaly detection for indoor air quality time-series data. IEEE Sensors Journal, 23(4), 3787-3800. doi: 10.1109/JSEN.2022.3230361
Section
Regular Session Papers
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