Large-scale fault diagnosis for on-board train systems

  • B. D. Netten
  • R. A. Vingerhoeds
Application Sessions
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1010)


A new approach is developed for fault diagnosis during different stages of development and operation of large train systems, incorporating case-based reasoning, conditional probabilities and indexing networks. Due to the size and complexity, the explicit, complete and accurate modelling of the on-board train systems is regarded impossible. The knowledge is implicitly available in fault-cases with possible symptoms, test results and actions. Off-line, different diagnostic systems are automatically maintained and (re)generated. Knowledge and experience of manufacturers and railway companies are fed back into all systems, but only after validation by authorised personnel. On-line, the system responses are consistent and fast enough, despite the size and uncertainty in the fault-cases. Available case-based reasoning tools have serious limitations in permissible size of the problem, handling probability factors, meeting required response times and satisfying the real-time requirements. The novelty of the proposed approach is that fault-networks, rather than fault-trees, are built automatically as the indexing structure of the case-base for on-line use.


case-based reasoning fault diagnosis network probabilities real-time 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Aamodt A., E. Plaza (1994). Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications, 7, nr. 1, March, pp. 39–52.Google Scholar
  2. Auriol E., M. Manago, K.D. Althoff, S. Wess, S. Dittrich (1994). Integrating Induction and Case-Based Reasoning: Methodolological Approach and First Evaluations. 2 nd European Workshop on Case-Based Reasoning, (eds.) M. Keane, J.P. Haton, M. Manago, Chantilly, 7–10 November, pp 145–156.Google Scholar
  3. Forgy, C.L. (1982). Rete: A fast Algorithm for the Many pattern/Many Object Pattern Match Problem. Artificial Intelligence, 19, pp. 17–37.Google Scholar
  4. Johnson, K. (1994). Evolution of the Trouble Shooting Manual for the A319/A320/A321/A330/A340 Central Maintenance System. FAST Airbus Technical Digest, nr. 16, pp. 10–15.Google Scholar
  5. Keane M., J.P. Haton, M. Manago, (Eds.) (1994). Second European Workshop on Case-Based Reasoning. AcknoSoft Press (Paris), Chantilly, 7–10 November.Google Scholar
  6. Myllymäki P., H. Tirri (1993). Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics. First European Workshop on Case-Based Reasoning, (eds.) M.M. Richter, S. Wess, K.-D. Althoff, F. Maurer, SEKI Report SR-93-12, University of Kaiserslautern, pp 48–53.Google Scholar
  7. Neapolitan R.E. (1990). Probabilistic Reasoning in Expert Systems: Theory and Algorithms. John Wiley & Sons.Google Scholar
  8. Netten B.D., R.A. Vingerhoeds (1994). Automatic Fault-Tree Generation, A Generic Approach for Fault Diagnosis Systems. IFAC Workshop Safety, Reliability and Applications of Emerging Intelligent Control Techniques, Hong Kong, 12–14 december, pp 182–187.Google Scholar
  9. Richter M.M., S. Wess, K.-D. Althoff, F. Maurer (Eds.) (1993). First European Workshop on Case-Based Reasoning. SEKI Report SR-93-12, University of Kaiserslautern, 1–5 November.Google Scholar
  10. Vepa R. (1992). Monitoring and Fault Diagnosis in Control Engineering. in: Application of Artificial Intelligence in Process Control, (eds.) L. Boullart, A. Krijgsman, R.A. Vingerhoeds. Pergamon Press, pp. 456–496.Google Scholar
  11. Woltering A., Schult T.J. (1993). Management Strategy Consultation Using a Case-Based Reasoning Shell. First European Workshop on Case-Based Reasoning. (eds.) M.M. Richter, S. Wess, K.-D. Althoff, F. Maurer, SEKI Report SR-93-12, University of Kaiserslautern, pp 227–232.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • B. D. Netten
    • 1
  • R. A. Vingerhoeds
    • 1
    • 2
  1. 1.Faculty of Technical Mathematics and Informatics, Knowledge Based Systems GroupDelft University of TechnologyBL DelftThe Netherlands
  2. 2.Department of Electrical and Electronic EngineeringUniversity of Wales SwanseaSwanseaUK

Personalised recommendations