Abstract
Timely fault detection and diagnosis are critical matters for modern chemical plants and refineries. Traditional approaches to fault detection and diagnosis of those complex systems produce centralized models that are very difficult to maintain. In this article, we introduce a biologically inspired multi-agent model which exploits the concept of leadership; that is, when a fault is detected one agent emerges as leader and coordinates the fault classification process. The proposed model is flexible, modular, decentralized, and portable. Our experimental results show that even using simple detection and diagnosis methods, the model can achieve comparable results to those from sophisticated centralized approaches.
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
Chiang, L.H., Russell, E.L., Braatz, R.D.: Fault Detection and Diagnosis in Industrial Systems. Springer, London (2001)
Crowder, J.A.: Multiple information agents for real-time ISHM: Architectures for real-time warfighter support. In: Proc. of Int. Conf. on Artificial Intelligence (2010)
Downs, J.J., Vogel, E.F.: A plant-wide industrial process control problem. Computers and Chemical Engineering 17(3), 245–255 (1993)
King, A., Cowlishaw, G.: Leaders, followers and group decision-making. Communicative & Integrative Biology 2(2), 147–150 (2009)
Mangina, E.E., McArthur, S.D.J., McDonald, J.R.: COMMAS (COndition Monitoring Multi-Agent System). Autonomous Agents and Multi-Agent Systems 4(3), 279–282 (2001)
Mendoza, B., Xu, P., Song, L.: A multi-agent model for fault diagnosis in petrochemical plants. In: Proc. of 2011 IEEE Sensors Applications Symposium (2011)
Perk, S., Teymour, F., Cinar, A.: Statistical monitoring of complex chemical processes using agent-based systems. Industrial & Engineering Chemistry Research 49(11), 5080–5093 (2010)
Raich, A., Cinar, A.: Multivariate statistical methods for monitoring continuous processes: Assessment of discrimination power of disturbance models and diagnosis of multiple disturbances. Chemometrics and Intelligent Laboratory Systems 30, 37–48 (1995)
Seng, N.Y., Srinivasana, R.: Multi-agent based collaborative fault detection and identification in chemical processes. Eng. Applications of Artificial Intelligence 23(6), 934–949 (2010)
Yu, C.H., Werfel, J., Nagpal, R.: Collective decision-making in multi-agent systems by implicit leadership. In: Proc. of 9th Int. Conf. on Autonomous Agents and Multiagent Systems (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mendoza, B., Xu, P., Song, L. (2011). A Multi-agent Model with Dynamic Leadership for Fault Diagnosis in Chemical Plants. In: Abraham, A., Corchado, J.M., González, S.R., De Paz Santana, J.F. (eds) International Symposium on Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 91. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19934-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-19934-9_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19933-2
Online ISBN: 978-3-642-19934-9
eBook Packages: EngineeringEngineering (R0)