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

Formal Design of Asynchronous Fault Detection and Identification Components using Temporal Epistemic Logic

Autonomous critical systems, such as satellites and space rovers, must be able to detect the occurrence of faults in order to ensure correct operation. This task is carried out by Fault Detection and Identification (FDI) components, that are embedded in those systems and are in charge of detecting faults in an automated and timely manner by reading data from sensors and triggering predefined alarms. The design of effective FDI components is an extremely hard problem, also due to the lack of a complete theoretical foundation, and of precise specification and validation techniques. In this paper, we present the first formal approach to the design of FDI components for discrete event systems, both in a synchronous and asynchronous setting. We propose a logical language for the specification of FDI requirements that accounts for a wide class of practical cases, and includes novel aspects such as maximality and trace-diagnosability. The language is equipped with a clear semantics based on temporal epistemic logic, and is proved to enjoy suitable properties. We discuss how to validate the requirements and how to verify that a given FDI component satisfies them. We propose an algorithm for the synthesis of correct-by-construction FDI components, and report on the applicability of the design approach on an industrial case-study coming from aerospace.Comment: 33 pages, 20 figure

A More General Theory of Diagnosis from First Principles

Model-based diagnosis has been an active research topic in different communities including artificial intelligence, formal methods, and control. This has led to a set of disparate approaches addressing different classes of systems and seeking different forms of diagnoses. In this paper, we resolve such disparities by generalising Reiter's theory to be agnostic to the types of systems and diagnoses considered. This more general theory of diagnosis from first principles defines the minimal diagnosis as the set of preferred diagnosis candidates in a search space of hypotheses. Computing the minimal diagnosis is achieved by exploring the space of diagnosis hypotheses, testing sets of hypotheses for consistency with the system's model and the observation, and generating conflicts that rule out successors and other portions of the search space. Under relatively mild assumptions, our algorithms correctly compute the set of preferred diagnosis candidates. The main difficulty here is that the search space is no longer a powerset as in Reiter's theory, and that, as consequence, many of the implicit properties (such as finiteness of the search space) no longer hold. The notion of conflict also needs to be generalised and we present such a more general notion. We present two implementations of these algorithms, using test solvers based on satisfiability and heuristic search, respectively, which we evaluate on instances from two real world discrete event problems. Despite the greater generality of our theory, these implementations surpass the special purpose algorithms designed for discrete event systems, and enable solving instances that were out of reach of existing diagnosis approaches

Methodologies for hybrid systems diagnosis based on the hybrid automaton framework

Hybrid systems play an important role in the modeling of complex systems since they take into account the interaction between both continuous dynamics and discrete events. Complex systems are subject to changes in the dynamics due to several factors such as nonlinearities, changes in the parameters, disturbances, faults, discrete events and controller actions among others. These facts lead to the need to develop a diagnostic system for hybrid systems improving the diagnostic precision. Hybrid systems allow to combine the classic fault detection and isolation approaches and a diagnoser based on discrete event models. Hence, a design methodology and implementation architecture for diagnosers in the framework of hybrid systems is proposed. The design methodology is based on the hybrid automaton model that represents the system behavior by means of the interaction of continuous dynamics and discrete events. The architecture is composed of modules which carry out mode recognition and diagnostic tasks interacting each other, since the diagnosis module adapts accordingly to the current hybrid system mode. The mode recognition task involves detecting and identifying a mode change by determining the set of residuals that are consistent with the current hybrid system mode. On the other hand, the diagnostic task involves detecting and isolating two type of faults: structural and non-structural faults. In the first case, structural faults are represented by a dynamic model as in the case of nominal modes. Hence they are identified by consistency checking through the set of residuals. In the second case, non-structural faults do not change the structure of the model, therefore, they are identified by a proper residual pattern. %the set of of residuals that can explain this inconsistency. Discernibility is the main property used in hybrid systems diagnosis. Through the concept of discernibility it is possible to predict whether modes changes (faulty or nominal) in the hybrid model can be detected and isolated properly. This concept can be applied in practice, evaluating a set of mathematical properties derived from residual expressions, which can be obtained from input-output models or parity space equations. General properties are derived to evaluate the discernibility between modes in the hybrid automaton model. The diagnoser is built through propagation algorithms developed for discrete models represented by automata. The automaton employed to build the diagnoser for a hybrid system is named behaviour automaton. It gathers all information provided by discernibility properties between modes and observable events in the system, increasing the system diagnosability. % in the system. Diagnosis for hybrid systems can be divided in two stages: offline and online. Moreover, it can be carried out twofold: in a non-incremental and an incremental form. In the non-incremental form, algorithms are executed taking into account global models, unlike incremental form that leads to building the useful parts of the diagnoser, only developing the branches that are needed to explain the occurrence of incoming events. The resulting diagnoser adapts to the system operational life and it is much less demanding in terms of memory storage than building the full diagnoser offline. The methodology is validated by the application to a case study based on a representative part of the Barcelona sewer network by means of a tool implemented in Matlab

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

Blaming in component-based real-time systems

International audienceIn component-based safety-critical real-time systems it is crucial to determine which com-ponent(s) caused the violation of a required system-level safety property, be it to issue a precise alert, or to determine liability of component providers. In this paper we present an approach for blaming in real-time systems whose component specifications are given as timed automata. The analysis is based on a single execution trace violating a safety property P. We formalize blaming using counterfactual reasoning ("what would have been the outcome if component C had behaved correctly?") to distinguish component failures that actually con-tributed to the outcome from failures that had no impact on the violation of P. We then show how to effectively implement blaming by reducing it to a model-checking problem for timed automata, and demonstrate the feasibility of our approach on the models of a pacemaker and of a chemical reactor

Tools and Algorithms for the Construction and Analysis of Systems

This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems