Abstract
The demand for high-performance solutions for anomaly detection and forecasting fault events is increasing in the industrial area. The detection and forecasting faults from time-series data are one critical mission in the Internet of Things (IoT) data mining. The classical fault detection approaches based on physical modelling are limited to some measurable output variables. Accurate physical modelling of vehicle dynamics requires substantial prior information about the system. On the other hand, data-driven modelling techniques accurately represent the system’s dynamic from data collection. Experimental results on large-scale data sets from Metro do Porto subsystems verify that our method performs high-quality fault detection and forecasting solutions. Also, health indicator obtained from the principal component analysis of the forecasting solution is applied to predict the remaining useful life.
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Acknowledgements
This work was supported by the CHIST-ERA grant CHIST-ERA-19-XAI-012, and project CHIST-ERA/0004/2019 funded by FCT.
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Davari, N., Veloso, B., Ribeiro, R.P., Gama, J. (2023). Fault Forecasting Using Data-Driven Modeling: A Case Study for Metro do Porto Data Set. In: Koprinska, I., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1753. Springer, Cham. https://doi.org/10.1007/978-3-031-23633-4_26
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DOI: https://doi.org/10.1007/978-3-031-23633-4_26
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