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Section: Scientific Foundations

Computer assisted monitoring and diagnosis of physical systems

Our work on monitoring and diagnosis relies on model-based approaches developed by the Artificial Intelligence community since the founding studies by R. Reiter and J. de Kleer [83] , [49] . Our project investigates the on-line monitoring and diagnosis of systems, which are modeled as discrete events systems, focusing more precisely on monitoring by alarms management [64] . Computational efficiency is a crucial issue for real size problems. We are developing two approaches. The first one relies on diagnoser techniques [88] , for which we have proposed a decentralized and generic approach. The second one uses chronicle recognition techniques, focusing on learning chronicles.

Early work on model-based diagnosis dates back to the 70-80's by R. Reiter, the reference papers on the logical theory of diagnosis being [83] , [49] . In the same years was constituted the community known as DX , named after the workshop on the principles of diagnosis . Research in these areas is still very active and the workshop gathers about fifty people in the field every year. As opposed to the expert system approach, which has been the leading approach for diagnosis (medical diagnosis for instance) before 1990, the model-based approach lies on a deep model representing the expected correct behavior of the system to be supervised or on a fault model. Instead of acquiring and representing an expertise from experts, the model-based approach uses the design models of industrial systems. The approach has been initially developed for electronic circuits repair [50] , focusing on off-line diagnosis of so-called static systems. Two main approaches have been proposed then: (i) the consistency-based approach, relying on a model of the expected correct behavior, which aims at detecting the components responsible for a discrepancy between the expected observations and the ones actually observed ; (ii) the abductive approach which relies on a model of the failures that might affect the system, and which identifies the failures or the faulty behavior explaining the anomalous observations. See the references [24] , [26] for a detailed exposition of these investigations.

Since 1990, the researchers in the field have studied dynamic system monitoring and diagnosis, in a similar way as researchers in control theory do. What characterizes the AI approach is the use of qualitative models instead of quantitative ones and the importance given to the search for the actual source/causes of the faulty behavior. Model-based diagnosis approaches rely on qualitative simulation or on causal graphs in order to look for the causes of the observed deviations. The links between the two communities have been enforced, in particular for what concerns the work about discrete events systems and hybrid systems. Used formalisms are often similar (automata, Petri nets ,...) [33] , [64] .

Our team focuses on monitoring and on-line diagnosis of discrete events systems and in particular on monitoring by alarm management. In this context, a human operator is generally in charge of the system monitoring and receives time-stamped events (the alarms) which are emitted by the components themselves, in reaction to external events. These observations on the system are discrete pieces of information, corresponding to an instantaneous event or to a property associated to a time interval. The main difficulties for analyzing this flow of alarms are the following:

There are two cases focusing on very different issues. In the first one, the alarms must be dealt with on-line by the operator. In this case, alarm analysis must be done in real time. The operator must react in a very short period of time to keep the system working at best in spite of the inputs variability and the natural evolution of the processes. Consequently, the natural system damages (components wear, slow modification of the components properties, etc.) are not directly taken into account but are corrected by tuning some parameters.

This reactive treatment withstands the treatment of alarms maintenance. In this second case, a deeper off line analysis of the system is performed, by foreseeing the possible difficulties, by planning the maintenance operations in order to minimize significantly the failures and interruptions of the system.

The major part of our work focuses on on-line monitoring aid and it is assumed that the correct behavior model or the fault models of the supervised systems are available. However, an on-line use of the models is rarely possible because of its complexity with respect to real time constraints. This is especially true when temporal models are under concern. A way to tackle this problem is to make an off-line transformation (or compilation) of the models and to extract, in an adapted way, the useful elements for diagnosis.

We study two different methods:

Developing diagnosis methodologies is not enough, especially when on-line monitoring is required. Two related concerns must be tackled, and are the topics of current research in the team:


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