Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Fuzzy classification with restricted Boltzman machines and echo-state networks for predicting potential railway door system failures
Authors: Fink, Olga
Zio, Enrico
Weidmann, Ulrich
DOI: 10.1109/TR.2015.2424213
Published in: IEEE Transactions on Reliability
Volume(Issue): 64
Issue: 3
Page(s): 861
Pages to: 868
Issue Date: 2015
Publisher / Ed. Institution: IEEE
ISSN: 0018-9529
Language: English
Subject (DDC): 004: Computer science
620: Engineering
Abstract: In this paper, a fuzzy classification approach applying a combination of Echo-State Networks (ESNs) and a Restricted Boltzmann Machine (RBM) is proposed for predicting potential railway rolling stock system failures using discrete-event diagnostic data. The approach is demonstrated on a case study of a railway door system with real data. Fuzzy classification enables the use of linguistic variables for the definition of the time intervals in which the failures are predicted to occur. It provides a more intuitive way to handle the predictions by the users, and increases the acceptance of the proposed approach. The research results confirm the suitability of the proposed combination of algorithms for use in predicting railway rolling stock system failures. The proposed combination of algorithms shows good performance in terms of prediction accuracy on the railway door system case study.
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

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.