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

Volume 11, Number 2, March 2015 - Paper 2 - pp. 121-134

OLGA FINK1,2, ENRICO ZIO3,4 and ULRICH WEIDMANN5

1 Institute of Data Analysis and Process Design, Zurich University of Applied Sciences (ZHAW), SWITZERLAND
2 Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, USA
3 Chair on Systems Science and the Energetic Challenge, European Foundation for New Energy-Electricité de France (EDF) at École Centrale Paris and SUPELEC, FRANCE
4 Department of Energy, Politecnico di Milano, ITALY
5 Institute for Transport Planning and Systems, ETH Zurich, SWITZERLAND

(Received on May 11, 2014, revised on September 25, 2014)

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.

 

References: 24

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