In the railway domain, rolling stock maintenance affects service operation time and efficiency. Minimizing train unavailability is essential for reducing capital loss and operational costs. To this aim, prediction of failures of rolling stock equipment is crucial to proactively trigger proper maintenance activities. Indeed, predictive maintenance is a golden example of the digital transformation within Industry 4.0, which affects several engineering processes in the railway domain. Nowadays, it may leverage artificial intelligence and machine learning algorithms to forecast failures and schedule the optimal time for maintenance actions. Generally, rail systems deteriorate gradually over time or fail directly, leading to data that vary extremely slowly. Indeed, ML approaches for predictive maintenance should consider this type of data to accurately predict and forecast failures. This paper proposes a methodology based on Long Short-Term Memory deep learning algorithms for predictive maintenance of railway rolling stock equipment. The methodology allows us to properly learn long-term dependencies for gradually changing data, and both predicting and forecasting failures of rail equipment. In the framework of an academic-industrial partnership, the methodology is experimented on a train traction converter cooling system, demonstrating its applicability and benefits. The results show that it outperforms state-of-the-art methods, reaching a failure prediction and forecasting accuracy over 99%, with a false alarm rate of ∼ 0.4% and a mean absolute error in the order of 10−4, respectively.

LSTM-based failure prediction for railway rolling stock equipment / DE SIMONE, Luigi; Caputo, Enzo; Cinque, Marcello; Galli, Antonio; Moscato, Vincenzo; Russo, Stefano; Cesaro, Guido; Criscuolo, Vincenzo; Giannini, Giuseppe. - In: EXPERT SYSTEMS WITH APPLICATIONS. - ISSN 0957-4174. - 222:119767(2023), pp. 1-10. [10.1016/j.eswa.2023.119767]

LSTM-based failure prediction for railway rolling stock equipment

Luigi De Simone
Primo
;
Enzo Caputo;Marcello Cinque;Antonio Galli;Vincenzo Moscato;Stefano Russo;
2023

Abstract

In the railway domain, rolling stock maintenance affects service operation time and efficiency. Minimizing train unavailability is essential for reducing capital loss and operational costs. To this aim, prediction of failures of rolling stock equipment is crucial to proactively trigger proper maintenance activities. Indeed, predictive maintenance is a golden example of the digital transformation within Industry 4.0, which affects several engineering processes in the railway domain. Nowadays, it may leverage artificial intelligence and machine learning algorithms to forecast failures and schedule the optimal time for maintenance actions. Generally, rail systems deteriorate gradually over time or fail directly, leading to data that vary extremely slowly. Indeed, ML approaches for predictive maintenance should consider this type of data to accurately predict and forecast failures. This paper proposes a methodology based on Long Short-Term Memory deep learning algorithms for predictive maintenance of railway rolling stock equipment. The methodology allows us to properly learn long-term dependencies for gradually changing data, and both predicting and forecasting failures of rail equipment. In the framework of an academic-industrial partnership, the methodology is experimented on a train traction converter cooling system, demonstrating its applicability and benefits. The results show that it outperforms state-of-the-art methods, reaching a failure prediction and forecasting accuracy over 99%, with a false alarm rate of ∼ 0.4% and a mean absolute error in the order of 10−4, respectively.
2023
LSTM-based failure prediction for railway rolling stock equipment / DE SIMONE, Luigi; Caputo, Enzo; Cinque, Marcello; Galli, Antonio; Moscato, Vincenzo; Russo, Stefano; Cesaro, Guido; Criscuolo, Vincenzo; Giannini, Giuseppe. - In: EXPERT SYSTEMS WITH APPLICATIONS. - ISSN 0957-4174. - 222:119767(2023), pp. 1-10. [10.1016/j.eswa.2023.119767]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/914277
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