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Journal Articles International Journal of Performability Engineering Year : 2015

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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Dates and versions

hal-01259645 , version 1 (20-01-2016)

Identifiers

  • HAL Id : hal-01259645 , version 1

Cite

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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