A general algorithm for pattern diagnosability of distributed discrete event systems

International audienceDiagnosability is an important system property that determines at design stage how accurate any diagnostic reasoning can be on a partially observed system. A fault in a system is diagnosable iff its occurrence can always be deduced from enough observations. The centralized diagnosability approaches lead to state explosion since they assume the existence of a monolithic model of the system. This is why very recently the distributed approaches for diagnosability began to be investigated, relying on local objects. On the other hand, diagnosis objectives are generalized from fault event to fault pattern that can represent multiple faults, repeating fault, sequences of significant events, etc. For pattern case, most existing approaches are centralized. In this paper, we propose a new distributed framework for pattern diagnosability. We first show how to recognize patterns by incrementally constructing local pattern recognizers. Then we propose a structure called regional pattern verifier constructed from the subsystem where the pattern is completely recognized before showing how to abstract the necessary and sufficient diagnosability information to further save the search space. Then the global consistency checking is based on another local structure called abstracted local twin checker to analyze pattern diagnosability. The correctness of our distributed algorithm is theoretically proved and its efficiency experimentally demonstrated by the results of the implementation

Twin‐engined diagnosis of discrete‐event systems

Diagnosis of discrete-event systems (DESs) is computationally complex. This is why a variety of knowledge compilation techniques have been proposed, the most notable of them rely on a diagnoser. However, the construction of a diagnoser requires the generation of the whole system space, thereby making the approach impractical even for DESs of moderate size. To avoid total knowledge compilation while preserving efficiency, a twin-engined diagnosis technique is proposed in this paper, which is inspired by the two operational modes of the human mind. If the symptom of the DES is part of the knowledge or experience of the diagnosis engine, then Engine 1 allows for efficient diagnosis. If, instead, the symptom is unknown, then Engine 2 comes into play, which is far less efficient than Engine 1. Still, the experience acquired by Engine 2 is then integrated into the symptom dictionary of the DES. This way, if the same diagnosis problem arises anew, then it will be solved by Engine 1 in linear time. The symptom dic- tionary can also be extended by specialized knowledge coming from scenarios, which are the most critical/probable behavioral patterns of the DES, which need to be diagnosed quickly

Discrete and hybrid methods for the diagnosis of distributed systems

Many important activities of modern society rely on the proper functioning of complex systems such as electricity networks, telecommunication networks, manufacturing plants and aircrafts. The supervision of such systems must include strong diagnosis capability to be able to effectively detect the occurrence of faults and ensure appropriate corrective measures can be taken in order to recover from the faults or prevent total failure. This thesis addresses issues in the diagnosis of large complex systems. Such systems are usually distributed in nature, i.e. they consist of many interconnected components each having their own local behaviour. These components interact together to produce an emergent global behaviour that is complex. As those systems increase in complexity and size, their diagnosis becomes increasingly challenging. In the first part of this thesis, a method is proposed for diagnosis on distributed systems that avoids a monolithic global computation. The method, based on converting the graph of the system into a junction tree, takes into account the topology of the system in choosing how to merge local diagnoses on the components while still obtaining a globally consistent result. The method is shown to work well for systems with tree or near-tree structures. This method is further extended to handle systems with high clustering by selectively ignoring some connections that would still allow an accurate diagnosis to be obtained. A hybrid system approach is explored in the second part of the thesis, where continuous dynamics information on the system is also retained to help better isolate or identify faults. A hybrid system framework is presented that models both continuous dynamics and discrete evolution in dynamical systems, based on detecting changes in the fundamental governing dynamics of the system rather than on residual estimation. This makes it possible to handle systems that might not be well characterised and where parameter drift is present. The discrete aspect of the hybrid system model is used to derive diagnosability conditions using indicator functions for the detection and isolation of multiple, arbitrary sequential or simultaneous events in hybrid dynamical networks. Issues with diagnosis in the presence of uncertainty in measurements due sensor or actuator noise are addressed. Faults may generate symptoms that are in the same order of magnitude as the latter. The use of statistical techniques,within a hybrid system framework, is proposed to detect these elusive fault symptoms and translate this information into probabilities for the actual operational mode and possibility of transition between modes which makes it possible to apply probabilistic analysis on the system to handle the underlying uncertainty present

A Scalable Jointree Algorithm for Diagnosability ∗

Diagnosability is an essential property that determines how accurate any diagnostic reasoning can be on a system given any sequence of observations. An unobservable fault event in a discrete-event system is diagnosable iff its occurrence can always be deduced once sufficiently many subsequent observable events have occurred. A classical approach to diagnosability checking constructs a finite state machine known as a twin plant for the system, which has a critical path iff some fault event is not diagnosable. Recent work attempts to avoid the often impractical construction of the global twin plant by exploiting system structure. Specifically, local twin plants are constructed for components of the system, and synchronized with each other until diagnosability is decided. Unfortunately, synchronization of twin plants can remain a bottleneck for large systems; in the worst case, in particular, all local twin plants would be synchronized, again producing the global twin plant. We solve the diagnosability problem in a way that exploits the distributed nature of realistic systems. In our algorithm consistency among twin plants is achieved by message passing on a jointree. Scalability is significantly improved as the messages computed are generally much smaller than the synchronized product of the twin plants involved. Moreover we use an iterative procedure to search for a subset of the jointree that is sufficient to decide diagnosability. Finally, our algorithm is scalable in practice: it provides an approximate and useful solution if the computational resources are not sufficient

A scalable jointree algorithm for diagnosability

Diagnosability is an essential property that determines how accurate any diagnostic reasoning can be on a system given any sequence of observations. An unobservable fault event in a discrete-event system is diagnosable iff its occurrence can always be deduced once sufficiently many subsequent observable events have occurred. A classical approach to diagnosability checking constructs a finite state machine known as a twin plant for the system, which has a critical path iff some fault event is not diagnosable. Recent work attempts to avoid the often impractical construction of the global twin plant by exploiting system structure. Specifically, local twin plants are constructed for components of the system, and synchronized with each other until diagnosability is decided. Unfortunately, synchronization of twin plants can remain a bottleneck for large systems; in the worst case, in particular, all local twin plants would be synchronized, again producing the global twin plant. We solve the diagnosability problem in a way that exploits the distributed nature of realistic systems. In our algorithm consistency among twin plants is achieved by message passing on a jointree. Scalability is significantly improved as the messages computed are generally much smaller than the synchronized product of the twin plants involved. Moreover we use an iterative procedure to search for a subset of the jointree that is sufficient to decide diagnosability. Finally, our algorithm is scalable in practice: it provides an approximate and useful solution if the computational resources are not sufficient

Une approche basée modèle pour l'optimisation du monitoring de systèmes avioniques relativement à leurs performances de diagnostic

Les systèmes avioniques s'étoffent et se complexifient de plus en plus. Avec l'augmentation des capacités de calcul, de nouvelles architectures basées sur le partage de ressources émergent. Effectuer le diagnostic d'un système n'est désormais plus une opération anodine. L'enjeu actuel est donc de mettre en place des techniques de diagnostic performantes tout en optimisant les capacités de monitoring nécessaires.Ce mémoire donne une caractérisation basée modèle d'un système sous diagnostic, puis propose des techniques pour en évaluer les performances de diagnostic, ainsi que celles de son monitoring (relativement à ces performances). Le contexte industriel dans lequel s'inscrit cette thèse amène d'autres contraintes, notamment la prise en compte de la taille des systèmes avioniques à analyser. Cette thèse étudie alors l'applicabilité des techniques introduites dans ce contexte et en propose une adaptation.Avionics systems become more and more complex. With the improvment of computing possibilities, new architectures based on resources sharing are growing up. Perform diagnosis of a system is no longer a trivial operation. The challenge is to develop efficient techniques of diagnosis while optimizing capabilities of monitoring required.This thesis give a model-based characterization of a system under diagnosis, and proposes techniques to assess diagnostic performances, as well as its monitoring ones (with respect to these diagnostic performances). The industrial context of this thesis brings other constraints, and in particular the need to handle the size of avionics systems to analyze. That thesis then examines the applicability of the introduced techniques to this particular context, and proposes an adaptation.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF