Abstract
Diagnosis is an important function of a holonic manufacturing system if the desired levels of stability, adaptability and flexibility are to be achieved. Our research agenda is to study holonic behaviours (such as diagnosis and control) through the incorporation of these behaviours into operational industrial systems. Given the lack of fielded holonic solutions in industry, we are currently constrained to use conventional systems in our work. In this paper we describe the development of a holonic diagnostic capability for a PLC-controlled vehicle assembly line. A novel model-based strategy is used for diagnosis. Because of the constraints imposed on model formation in this environment, a two-phase approach consisting of off-line fault space generation and online fault space analysis is used. The fault space analysis utilises heuristics to achieve the desired performance levels (diagnosis in less than 60 seconds and success rates of greater than 90%). Areas for further research in holonic diagnosis are identified.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
A. Koestler: The Ghost in the Machine, Arkana, London (1967).
W.B. Day and M.J. Rostosky: “Diagnostic Expert Systems for PLC Controlled Manufacturing Equipment”, International Journal of Computer Integrated Manufacturing, 7, 116–122 (1994).
A. Cohn: “Qualitative Reasoning”, in Lecture Notes in Artificial Intelligence, No. 345, Springer, Berlin, Heidelberg (1988), pp. 60–95.
R. Davis: “Diagnostic Reasoning Based on Structure and Behaviour”, Artificial Intelligence, 24, 347–410 (1984).
J. de Kleer and B. Williams: “Diagnosing Multiple Faults”, Artificial Intelligence, 32, 97–130 (1987).
I. Bratko, I. Mozetic and N. Lavrac: Kardio: A Study in Deep and Qualitative Knowledge for Expert Systems, MIT Press, Cambridge MA (1989).
J. de Kleer: “An Assumption-based TMS”, Artificial Intelligence 28, 127–162 (1986).
J. de Kleer: “Problem Solving with the ATMS”, Artificial Intelligence, 28, 197–224 (1986).
D. Pearce: “The Induction of Fault Diagnosis Systems from Qualitative Models”, in Proceedings of AAAI-88, AAAI Press, Menlo Park CA (1988), pp. 353–357.
B. Williams and P. Nayak: “Immobile Robots: AI in the New Millennium”, AI Magazine, 17, 16–35 (1996).
V. Saraswat, D. Bobrow and J. de Kleer: Infrastructure for Model-Based Computing, Xerox PARC (1993).
K.D. Forbus and J. de Kleer: Building Problem Solvers, MIT Press, Cambridge MA (1993).
H. Wong, M. Fromherz, V. Gupta and V. Saraswat: “Control-Based Programming of Electro-Mechanical Controllers”, in Proceedings of IJCAI Workshop on Executable Temporal Logics, Montreal (1995).
J.H. Jarvis and D.H. Jarvis: “Design Recovery for PLC Controlled Manufacturing Systems”, in Manufacturing Systems: Modelling, Management and Control, ed. P. Kopacek, Elsevier (1998).
J.H. Jarvis and D.H. Jarvis: “Life Cycle Support for PLC Controlled Manufacturing Systems”, in Software Engineering for Manufacturing Systems, eds. A. Storr and D. Jarvis, Chapman and Hall, London (1996).
J.H. Jarvis and D.H. Jarvis: “Simulation of a PLC Controlled Assembly Line”, in Simulation in Industry: 9th European Simulation Symposium, eds. W. Hahn and A. Lehmann, SCS (1997).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Jarvis, D.H., Jarvis, J.H. (2003). Holonic Diagnosis for an Automotive Assembly Line. In: Deen, S.M. (eds) Agent-Based Manufacturing. Advanced Information Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-05624-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-662-05624-0_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-07895-8
Online ISBN: 978-3-662-05624-0
eBook Packages: Springer Book Archive