Model-based Supervision of Composite Systems
|Anbulagan||Adi Botea||Alban Grastien
||Jussi Rintanen||Sylvie Thiébaux|
|Andre Cire||Priscilla Kan John||Elena Kelareva ||Anika Schumann ||Sajjad Siddiqi
Our research provides assistance with the resolution of three main problems by system designers and operators:
We are developing a unique approach is based on recent advances in the fields of artificial intelligence, automated verification, and discrete-event systems, most notably model-based diagnosis, planning, and model-checking. The approach heavily relies on decentralised and symbolic algorithms.
What will this research achieve?
An improvement in the quality of service from complex dynamic systems in telecommunications, power, computer, transport, and manufacturing industries.
Who will benefit?
Both industry and the consumers of their services and products, from electricity to manufactured goods.
What are the key features?
Our technology is targeted at complex systems in which faults cannot be identified via a small number of obvious indicators. Instead, a complex combination of indicators, possibly over a long time span, has to be observed and analysed in order to infer a faulty behaviour. Our approach is model-based, which means that correct, accurate and efficient supervision methods can be obtained.
The inputs to the software are:
Our technology is generic but can be tailored to different applications, including manufacturing, power distribution, computer networks, transportation, smart houses. It addresses 3 critical technical issues:
Diagnosis systems in use in industry typically rely on alarm correlation and expert reasoning. They are difficult to maintain as any change in a component or the system's organisation may invalidate the expertise. This can lead to prohibitive re-development costs.
In contrast, we use model-based reasoning from a library of component models. When a component is upgraded or added to the system, only its model changes or gets added to the component library. The monitoring software does not change. Our technology is applicable to reconfigurable systems.
The technology can be tailored to applications with different real-time and space requirements. This is achieved by considering a spectrum of methods. These range from slower but space-efficient cooperative methods based on the component models, to the automatic off-line compilation of the component models to diagnosers and controllers that provide efficient on-line supervision.
State of the art model-based diagnosis techniques typically handle discrete-event systems of the order of 10^6 states. Our approach scales up to 10^100 states. This is achieved via:
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Last Modified April 2016