Section: Research Program

Computer assisted monitoring and diagnosis of physical systems

keywords: monitoring,diagnosis, deep model, fault model, simulation, chronicle acquisition

Our work on monitoring and diagnosis relies on model-based approaches developed by the Artificial Intelligence community since the seminal studies by R. Reiter and J. de Kleer [78] , [89] . Two main approaches have been proposed then: (i) the consistency-based approach, relying on a model of the expected correct behavior ; (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 [29] , [31] 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 ,...) [37] , [35] .

Our team focuses on monitoring and on-line diagnosis of discrete events systems and in particular on monitoring by alarm management.

Two different methods have been studied by our team in the last years:

• In the first method, the automaton used as a model is transformed off-line into an automaton adapted to diagnosis. This automaton is called a diagnoser. This method has first been proposed by M. Sampath and colleagues [80] . The main drawback of this approach is its centralized nature that requires to explicitly build the global model of the system, which is most of the time unrealistic. It is why we proposed a decentralized approach in [75] .

• In the second method, the idea is to associate each failure that we want to detect with a chronicle (or a scenario), i.e. a set of observable events interlinked by time constraints. The chronicle recognition approach consists in monitoring and diagnosing dynamic systems by recognizing those chronicles on-line [53] , [77] , [51] .

  One of our research focus is to extend the chronicle recognition methods to a distributed context. Local chronicle bases and local recognizers are used to detect and diagnose each component. However, it is important to take into account the interaction model (messages exchanged by the components). Computing a global diagnosis requires then to check the synchronisation constraints between local diagnoses.

  Another issue is the chronicle base acquisition. An expert is often needed to create the chronicle base, and that makes the creation and the maintenance of the base very expensive. That is why we are working on an automatic method to acquire the base.

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:

• The ultimate goal is usually not merely to diagnose, but to put the system back in some acceptable state after the occurrence of a fault. One of our aim is to develop self-healable systems able to self-diagnose and -repair.

• When designing a system and equipping it with diagnosis capabilities, it may be crucial to be able to check off-line that the system will behave correctly, i.e., that the system is actually 'diagnosable'. A lot of techniques have been developed in the past (see Lafortune and colleagues [79] ), essentially in automata models. We extended them to cope with temporal patterns. A recent focus has been to study the self-healability of systems (ability to self-diagnose and self-repair).