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Development and Application of Deep Belief Networks for Predicting Railway Operation Disruptions

Abstract : In this paper, we propose to apply deep belief networks (DBN) to predict potential operational disruptions caused by rail vehicle door systems. DBN are a powerful algorithm that is able to detect and extract complex patterns and features in data and has demonstrated superior performance on several benchmark studies. A case study is shown whereby the DBN are trained and applied on real case study from a railway vehicle fleet. The DBN were shown to outperform a feedforward neural network trained by a genetic algorithm.
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Submitted on : Wednesday, January 20, 2016 - 5:21:39 PM
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  • HAL Id : hal-01259645, version 1

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Olga Fink, Enrico Zio, Ulrich Weidmann. Development and Application of Deep Belief Networks for Predicting Railway Operation Disruptions. International Journal of Performability Engineering, 2015, 11 (2), pp.121-134. ⟨hal-01259645⟩

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