Using LNT Formal Descriptions for Model-Based Diagnosis

International audienceProviding models for model-based diagnosis has always been a challenging task. There has never been an agreement on an underlying modeling language, making it almost impossible to share models within our community. In addition, there are other domains like formal methods or model-based testing relying on system models for formal verification and automated test case generation. Although, there we face the situation of different modeling languages as well, the question remains whether it is possible to re-use these models in the context of model-based diagnosis. In this paper , we elaborate on this question and show how models written in LNT can be used for fault local-ization only requiring simple modification. This allows re-using formal method's models for diagnosis directly. Besides discussing the underlying principles, we also present a use case showing the applicability of the methods

Model-based monitoring and diagnosis of systems with software-extended behavior

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005.Includes bibliographical references (p. 107-112).Model-based diagnosis of devices has largely operated on hardware systems. However, in most complex systems today, such as aerospace vehicles, automobiles and medical devices, hardware is augmented with software functions that influence the system's behavior. As these sophisticated systems are required to perform increasingly ambitious tasks. there is a growing need to ensure their robustness and safety. Prior work introduced probabilistic, hierarchical, constraint automata (PHCA), to allow compact encoding of both hardware and software behavior. The contribution of this thesis is a capability for monitoring and diagnosing software-extended systems in the presence of delayed symptoms, based on the expressive PHCA modeling formalism. Hardware models are extended to include the behavior of associated embedded software, resulting in more comprehensive diagnoses. This work introduces a novel approach that frames diagnosis over a finite time horizon as a soft constraint optimization problem (COP), which is then decomposed into independent subproblems using tree decomposition techniques. There are two advantages to this approach. First, the approach enables finite-horizon diagnosis in the presence of delayed symptoms. Second, the soft COP formulation provides convenient expressivity for encoding the PHCA models and their execution semantics, and enables the use of decomposition-based, efficient optimal constraint solvers. The solutions to the COP correspond to the most likely state trajectories of the software- extended system.(cont.) These state trajectories are enumerated and tracked within the finite receding horizon, as observations and issued commands become available. The diagnostic capability has been implemented and demonstrated on several scenarios from the aerospace and robotic domains, including vision-based rover navigation, the global metrology subsystem of the MIT SPHERES satellites, and models of the NASA New Millennium Earth Observing One (EO-1) spacecraft.by Tsoline Mikaelian.S.M

Distributed mode estimation through constraint decomposition

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 131-134).Large-scale autonomous systems such as modern ships or spacecrafts require reliable monitoring capabilities. One of the main challenges in large-scale system monitoring is the difficulty of reliably and efficiently troubleshooting component failure and deviant behavior. Diagnosing large-scale systems is difficult because of the fast increase in combinatorial complexity. Hence, efficient problem encoding and knowledge propagation between time steps is crucial. Moreover, concentrating all the diagnosis processing power in one machine is risky, as it creates a potential critical failure point. Therefore, we want to distribute the online estimation procedure. We introduce here a model-based method that performs robust, online mode estimation of complex, hardware or software systems in a distributed manner. Prior work introduced the concept of probabilistic hierarchical constraint automata (PHCA) to compactly model both complex software and hardware behavior. Our method, inspired by this previous work, translates the PHCA model to a constraint representation. This approach handles a more precise initial state description, scales to larger systems, and to allow online belief state updates. Additionally, a tree-clustering of the dual constraint graph associated with the multi-step trellis diagram representation of the system makes the search distributable. Our search algorithm enumerates the optimal solutions of a hard-constraint satisfaction problem in a best-first order by passing local constraints and conflicts between neighbor sub-problems of the decomposed global problem. The solutions computed online determine the most likely trajectories in the state space of a system. Unlike prior work on distributed constraint solving, we use optimal hard constraint satisfaction problems to increase encoding compactness. We present and demonstrate this approach on a simple example and an electric power-distribution plant model taken from a naval research project involving a large number of modules. We measure the overhead caused by distributing mode estimation and analyze the practicality of our approach.by Henri Badaro.S.M