Distributed Quantitative and Qualitative Fault Diagnosis: railway junction case study

Clive Roberts, Colin Goodman, Hemendra Dassanayake, Nadeem Lehrasab

Research output: Contribution to journalArticle

61 Citations (Scopus)


The paper develops a novel, comprehensive. reasoned approach to fault diagnosis in a class of reciprocating, electro-mechanical equipment referred to as single throw mechanical equipment (STME). STMEs are widely used in many industrial applications-examples of which include automatic doors, mechanical presses and barrier systems. A formal definition of the STME is initially presented. In this paper, an electro-pneumatic railway point machine within a railway junction is taken as a case study. The proposed approach distributes the fault diagnosis process across a geographical area interconnected using fieldbus data communication networks. This allows for the fault detection and isolation of multiple assets within close proximity to one another at a minimal cost. A robust, straightforward, quantitative method based on abstract static models and structured residuals is used for fault detection and preliminary diagnosis is suggested. This is capable of being implemented at an asset level on an embedded processor. Fault isolation is carried out at one central point in the distributed architecture; thus allowing full fault diagnosis of multiple assets to be carried out economically. Details of a hybrid quantitative and qualitative, neuro-fuzzy network suitable for carrying out fault isolation is discussed and results collected from a laboratory based test-rig are presented. (C) 2002 Elsevier Science Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)419-429
Number of pages11
JournalControl Engineering Practice
Issue number4
Publication statusPublished - 1 Apr 2002


  • distributed detection
  • fault diagnosis
  • fieldbus
  • railways
  • maintenance
  • fuzzy hybrid systems
  • qualitative analysis


Dive into the research topics of 'Distributed Quantitative and Qualitative Fault Diagnosis: railway junction case study'. Together they form a unique fingerprint.

Cite this