Data-driven predictive maintenance framework for railway systems
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Data-driven predictive maintenance framework for railway systemsAutor(es)
Data
2023Cita bibliográfica
Meira, J., Veloso, B., Bolón-Canedo, V., Marreiros, G., Alonso-Betanzos, A., & Gama, J. (2023). Data-driven predictive maintenance framework for railway systems. Intelligent Data Analysis, 27(4), 1087–1102. https://doi.org/10.3233/ida-226811
Resumo
[Abstract]: The emergence of the Industry 4.0 trend brings automation and data exchange to industrial manufacturing. Using computational systems and IoT devices allows businesses to collect and deal with vast volumes of sensorial and business process data. The growing and proliferation of big data and machine learning technologies enable strategic decisions based on the analyzed data. This study suggests a data-driven predictive maintenance framework for the air production unit (APU) system of a train of Metro do Porto. The proposed method assists in detecting failures and errors in machinery before they reach critical stages. We present an anomaly detection model following an unsupervised approach, combining the Half-Space-trees method with One Class K Nearest Neighbor, adapted to deal with data streams. We evaluate and compare our approach with the Half-Space-Trees method applied without the One Class K Nearest Neighbor combination. Our model produced few type-I errors, significantly increasing the value of precision when compared to the Half-Space-Trees model. Our proposal achieved high anomaly detection performance, predicting most of the catastrophic failures of the APU train system.
Palabras chave
Anomaly detection
Data streams
Unsupervised learning
One class classification
Predictive maintenance
Data streams
Unsupervised learning
One class classification
Predictive maintenance
Descrición
This version of the article has been accepted for publication, after peer review. The Version of Record is available online at: https://doi.org/10.3233/ida-226811.
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ISSN
1088-467X
1571-4128
1571-4128