INCREMENTAL FAULT DIAGNOSABILITY AND SECURITY/PRIVACY VERIFICATION

Dynamical systems can be classified into two groups. One group is continuoustime systems that describe the physical system behavior, and therefore are typically modeled by differential equations. The other group is discrete event systems (DES)s that represent the sequential and logical behavior of a system. DESs are therefore modeled by discrete state/event models.DESs are widely used for formal verification and enforcement of desired behaviors in embedded systems. Such systems are naturally prone to faults, and the knowledge about each single fault is crucial from safety and economical point of view. Fault diagnosability verification, which is the ability to deduce about the occurrence of all failures, is one of the problems that is investigated in this thesis. Another verification problem that is addressed in this thesis is security/privacy. The two notions currentstate opacity and current-state anonymity that lie within this category, have attracted great attention in recent years, due to the progress of communication networks and mobile devices.Usually, DESs are modular and consist of interacting subsystems. The interaction is achieved by means of synchronous composition of these components. This synchronization results in large monolithic models of the total DES. Also, the complex computations, related to each specific verification problem, add even more computational complexity, resulting in the well-known state-space explosion problem.To circumvent the state-space explosion problem, one efficient approach is to exploit the modular structure of systems and apply incremental abstraction. In this thesis, a unified abstraction method that preserves temporal logic properties and possible silent loops is presented. The abstraction method is incrementally applied on the local subsystems, and it is proved that this abstraction preserves the main characteristics of the system that needs to be verified.The existence of shared unobservable events means that ordinary incremental abstraction does not work for security/privacy verification of modular DESs. To solve this problem, a combined incremental abstraction and observer generation is proposed and analyzed. Evaluations show the great impact of the proposed incremental abstraction on diagnosability and security/privacy verification, as well as verification of generic safety and liveness properties. Thus, this incremental strategy makes formal verification of large complex systems feasible

Efficient Detection on Stochastic Faults in PLC Based Automated Assembly Systems With Novel Sensor Deployment and Diagnoser Design

In this dissertation, we proposed solutions on novel sensor deployment and diagnoser design to efficiently detect stochastic faults in PLC based automated systems First, a fuzzy quantitative graph based sensor deployment was called upon to model cause-effect relationship between faults and sensors. Analytic hierarchy process (AHP) was used to aggregate the heterogeneous properties between sensors and faults into single edge values in fuzzy graph, thus quantitatively determining the fault detectability. An appropriate multiple objective model was set up to minimize fault unobservability and cost while achieving required detectability performance. Lexicographical mixed integer linear programming and greedy search were respectively used to optimize the model, thus assigning the sensors to faults. Second, a diagnoser based on real time fuzzy Petri net (RTFPN) was proposed to detect faults in discrete manufacturing systems. It used the real time PN to model the manufacturing plant while using fuzzy PN to isolate the faults. It has the capability of handling uncertainties and including industry knowledge to diagnose faults. The proposed approach was implemented using Visual Basic, and tested as well as validated on a dual robot arm. Finally, the proposed sensor deployment approach and diagnoser were comprehensively evaluated based on design of experiment techniques. Two-stage statistical analysis including analysis of variance (ANOVA) and least significance difference (LSD) were conducted to evaluate the diagnosis performance including positive detection rate, false alarm, accuracy and detect delay. It illustrated the proposed approaches have better performance on those evaluation metrics. The major contributions of this research include the following aspects: (1) a novel fuzzy quantitative graph based sensor deployment approach handling sensor heterogeneity, and optimizing multiple objectives based on lexicographical integer linear programming and greedy algorithm, respectively. A case study on a five tank system showed that system detectability was improved from the approach of signed directed graph's 0.62 to the proposed approach's 0.70. The other case study on a dual robot arm also show improvement on system's detectability improved from the approach of signed directed graph's 0.61 to the proposed approach's 0.65. (2) A novel real time fuzzy Petri net diagnoser was used to remedy nonsynchronization and integrate useful but incomplete knowledge for diagnosis purpose. The third case study on a dual robot arm shows that the diagnoser can achieve a high detection accuracy of 93% and maximum detection delay of eight steps. (3) The comprehensive evaluation approach can be referenced by other diagnosis systems' design, optimization and evaluation

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

Fourier-Motzkin methods for fault diagnosis in discrete event systems

The problem of fault diagnosis under partial observation is a complex problem; and the challenge to solve this problem is to find a compromise between the space complexity and time complexity. The classic method to solve the problem is by constructing an automaton called a diagnoser. This method suffers from the state explosion problem which limits its application to large systems. In this thesis, the problem of fault diagnosis in partially observed discrete event systems is addressed. We assume that the system is modelled by Petri nets having no cycle of unobservable transitions. The class of labelled Petri nets is also considered with both bounded and unbounded cases. We propose a novel approach for fault diagnosis using the Integer Fourier-Motzkin Elimination method. The main idea is to reduce the problem of constructing the diagnoser to a problem of projecting between two spaces. In other words, we first obtain a set of inequalities derived from the state equation of Petri nets. Then, the elimination method is used to drop the variables corresponding to the unobservable transitions and we design two sets of inequalities in variables representing the observable transitions. One set ensures that the fault has occurred, whereas the other ensures that fault has not occurred. Given these two sets, we have proved that the occurrences of faults can be decided as any other diagnoser can do. The obtained result are extended to diagnose violations of constraints such as service level agreement and Quality of Service, which is of particular interested in telecommunication companies. We implement our approach and demonstrate gains in performance with respect to existing approaches on a benchmark example

Multi-resolution fault diagnosis in discrete-event systems

In this thesis, a framework for multi-resolution fault diagnosis in discrete-event systems (DES) is introduced. Here a sequence of plant models, with increasing resolution, are used in fault diagnosis and the range of possible diagnosis is narrowed down step by step, until the failure node is isolated. In this way, the original problem of fault diagnosis is replaced by a sequence of smaller problems. The plant models used at each step of diagnosis are abstractions of the original plant model. We propose to use model reduction through the solutions of the Relational Coarsest Partition problem to obtain these abstractions. For each diagnosis step, minimal sensor sets are chosen to have a coarser output map, and hence, to improve the efficiency of model reduction. In this thesis, a polynomial algorithm is proposed that verifies failure diagnosability by examining the distinguishability of two plant (normal/faulty) conditions at a time. A procedure is presented that finds minimal sensor sets, referred to as minimal distinguishes for distinguishability of one condition from another. A polynomial procedure is introduced that combines minimal distinguishers to obtain a minimal sensor set for fault diagnosis. The proposed method reduces the computational complexity of sensor selection. A benefit of using minimal distinguishers is that their computation maybe speeded up using expert knowledge. The proposed method for sensor selection is particularly suitable for multi-resolution diagnosis since it permits some of the results of computations, performed for sensor selection at the lowest (finest) level of multi-resolution diagnosis to be reduced at higher levels. This feature is particularly useful in reducing the computations necessary for online reconfiguration of the multi-resolution diagnosis system. An important procedure used in sensor selection is testing diagnosability. In this thesis, a new procedure for testing diagnosability in timed DES is introduced based on the relatively timing of plant output sequence. It is shown through example that the proposed test maybe executed with significantly fewer computations compared to tests developed for untimed models and adapted for timed systems. Furthermore, two new sets of sufficient conditions are provided under which diagnoser design and diagnosability tests based on relative timing of output sequence can be performed efficientl

DETECCIÓN ACTIVA DE FALTAS EN SISTEMAS DE EVENTOS DISCRETOS

ResumenEl objetivo de este trabajo es presentar una propuesta de solución de Diagnóstico Activo en Sistemas de Eventos Discretos modelado por redes de Petri. La propuesta se basa en un controlador llamado Circuito de Regulación Inteligente que reduce la distancia relativa entre las transiciones que modelan faltas y las del resto de la red de Petri, permitiendo la detección y diagnóstico de faltas mientras se mantiene la vivacidad del sistema y se reduce la flexibilidad del sistema sólo en los estados requeridos. Finalmente, los resultados presentados se ilustran en un ejemplo.Palabras Claves: Detección activa, diagnosticabilidad, redes de Petri, sistemas de eventos discretos. ACTIVE FAULT DETECTION IN DISCRETE EVENT SYSTEMSAbstractThe aim of this work is to present a proposal of Active Diagnosis in Discrete Event Systems modeled by Petri nets. This approach is based on a controller named Intelligent Regulation Circuit which reduces the relative distance among the system transition allow in the detection and diagnosis of faults while maintaining the liveness of the system. Finally, the results presented are illustrated by an example.Keywords: Active detection, diagnosability, discrete event system, Petri nets

Une approche efficace pour l’étude de la diagnosticabilité et le diagnostic des SED modélisés par Réseaux de Petri labellisés : contextes atemporel et temporel

This PhD thesis deals with fault diagnosis of discrete event systems using Petri net models. Some on-the-fly and incremental techniques are developed to reduce the state explosion problem while analyzing diagnosability. In the untimed context, an algebraic representation for labeled Petri nets (LPNs) is developed for featuring system behavior. The diagnosability of LPN models is tackled by analyzing a series of K-diagnosability problems. Two models called respectively FM-graph and FM-set tree are developed and built on the fly to record the necessary information for diagnosability analysis. Finally, a diagnoser is derived from the FM-set tree for online diagnosis. In the timed context, time interval splitting techniques are developed in order to make it possible to generate a state representation of labeled time Petri net (LTPN) models, for which techniques from the untimed context can be used to analyze diagnosability. Based on this, necessary and sufficient conditions for the diagnosability of LTPN models are determined. Moreover, we provide the solution for the minimum delay ∆ that ensures diagnosability. From a practical point of view, diagnosability analysis is performed on the basis of on-the-fly building of a structure that we call ASG and which holds fault information about the LTPN states. Generally, using on-the-fly analysis and incremental technique makes it possible to build and investigate only a part of the state space, even in the case when the system is diagnosable. Simulation results obtained on some chosen benchmarks show the efficiency in terms of time and memory compared with the traditional approaches using state enumerationCette thèse s'intéresse à l'étude des problèmes de diagnostic des fautes sur les systèmes à événements discrets en utilisant les modèles réseau de Petri. Des techniques d'exploration incrémentale et à-la-volée sont développées pour combattre le problème de l'explosion de l'état lors de l'analyse de la diagnosticabilité. Dans le contexte atemporel, la diagnosticabilité de modèles RdP-L est abordée par l'analyse d'une série de problèmes K-diagnosticabilité. L'analyse de la diagnosticabilité est effectuée sur la base de deux modèles nommés respectivement FM-graph et FM-set tree qui sont développés à-la-volée. Un diagnostiqueur peut être dérivé à partir du FM-set tree pour le diagnostic en ligne. Dans le contexte temporel, les techniques de fractionnement des intervalles de temps sont élaborées pour développer représentation de l'espace d'état des RdP-LT pour laquelle des techniques d'analyse de la diagnosticabilité peuvent être utilisées. Sur cette base, les conditions nécessaires et suffisantes pour la diagnosticabilité de RdP-LT ont été déterminées. En pratique, l'analyse de la diagnosticabilité est effectuée sur la base de la construction à-la-volée d'une structure nommée ASG et qui contient des informations relatives à l'occurrence de fautes. D'une manière générale, l'analyse effectuée sur la base des techniques à-la-volée et incrémentale permet de construire et explorer seulement une partie de l'espace d'état, même lorsque le système est diagnosticable. Les résultats des simulations effectuées sur certains benchmarks montrent l'efficacité de ces techniques en termes de temps et de mémoire par rapport aux approches traditionnelles basées sur l'énumération des état