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The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

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COMMAS (COndition Monitoring Multi-Agent System)

Mangina, E. and McArthur, S.D.J. and McDonald, J.R. (2001) COMMAS (COndition Monitoring Multi-Agent System). Autonomous Agents and Multi-Agent Systems, 4 (3). pp. 279-282. ISSN 1387-2532

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Abstract

The application of intelligent systems for data interpretation and condition monitoring is an advancing field of research. In recent years autonomous intelligent agents and multi-agent systems have gained much attention within different real time applications. The novel idea of COMMAS (COndition Monitoring Multi-Agent System) introduces a hierarchical decentralised multi-agent architecture developed for data interpretation and condition monitoring applications. By definition condition monitoring is concerned with detecting and distinguishing faults occurring in plant that is being monitored [1]; therefore the early diagnosis and identification of faults has a number of benefits (improvement in the plant economy, reduction in operational costs, improving the level of safety etc). A variety of intelligent techniques have been applied in plant monitoring, which resulted in the development of centralised approaches for condition monitoring, e.g., Knowledge Based Systems (KBS) [2], Model Based Reasoning (MBR) Systems [3], Case Based Reasoning (CBR) Systems [4], Artificial Neural Networks (ANN) [5] etc. These approaches tend to be fixed, so they lack flexibility and extensibility. Moving to an agent-based architecture allows simultaneous complex tasks to be performed in real-time; better handling of inaccurate data is achieved and each agent can be independently updated.