| Publication type: | Article in scientific journal |
| Type of review: | Peer review (publication) |
| Title: | Development and application of deep belief networks for predicting railway operations disruptions |
| Authors: | Fink, Olga Zio, Enrico Weidmann, Ulrich |
| Published in: | International Journal of Performability Engineering |
| Volume(Issue): | 11 |
| Issue: | 2 |
| Pages: | 121 |
| Pages to: | 134 |
| Issue Date: | 2015 |
| Publisher / Ed. Institution: | RAMS Consultants |
| ISSN: | 0973-1318 |
| Language: | English |
| Subject (DDC): | 004: Computer science 620: Engineering |
| 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/13894 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Data Analysis and Process Design (IDP) |
| Appears in collections: | Publikationen School of Engineering |
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