A step toward reusable model fragments.

In this paper we describe a system to elaborate models which are suitable for model based reasoning. A set of model fragments selected from a library will be put together to build a model candidate. The system relies on the bond graph notation, which allows a uniform approach for the different physical domains and offers a compositional view of the system. Modeling requires the exploration of a search space of potential model candidates. These models are checked to be consistent with a set of behavior constraints and modeling hypotheses provided by the user

Automatic modelling for diagnosis.

Much of the past work on fault diagnosis did not pay enough attention to model construction and its important role in aiding problem solving. It was generally accepted that a model was available or was assumed to be present in a certain format before starting the diagnosis process. However in practice a model which can be constructed from an engineering or commercial system is often different from the model on which diagnostic algorithms have been developed. Our paper aims at filling this gap between the model construction and model-based fault diagnosis, providing a framework to integrate them coherently

Operator procedure verification with a rapidly reconfigurable simulator

Generating and testing procedures for controlling spacecraft subsystems composed of electro-mechanical and computationally realized elements has become a very difficult task. Before a spacecraft can be flown, mission controllers must envision a great variety of situations the flight crew may encounter during a mission and carefully construct procedures for operating the spacecraft in each possible situation. If, despite extensive pre-compilation of control procedures, an unforeseen situation arises during a mission, the mission controller must generate a new procedure for the flight crew in a limited amount of time. In such situations, the mission controller cannot systematically consider and test alternative procedures against models of the system being controlled, because the available simulator is too large and complex to reconfigure, run, and analyze quickly. A rapidly reconfigurable simulation environment that can execute a control procedure and show its effects on system behavior would greatly facilitate generation and testing of control procedures both before and during a mission. The How Things Work project at Stanford University has developed a system called DME (Device Modeling Environment) for modeling and simulating the behavior of electromechanical devices. DME was designed to facilitate model formulation and behavior simulation of device behavior including both continuous and discrete phenomena. We are currently extending DME for use in testing operator procedures, and we have built a knowledge base for modeling the Reaction Control System (RCS) of the space shuttle as a testbed. We believe that DME can facilitate design of operator procedures by providing mission controllers with a simulation environment that meets all these requirements

Compositional Model Repositories via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences

The predominant knowledge-based approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the constituent interacting components of a system and translates it into a useful mathematical model. This paper presents a novel compositional modelling approach aimed at building model repositories. It furthers the field in two respects. Firstly, it expands the application domain of compositional modelling to systems that can not be easily described in terms of interacting functional components, such as ecological systems. Secondly, it enables the incorporation of user preferences into the model selection process. These features are achieved by casting the compositional modelling problem as an activity-based dynamic preference constraint satisfaction problem, where the dynamic constraints describe the restrictions imposed over the composition of partial models and the preferences correspond to those of the user of the automated modeller. In addition, the preference levels are represented through the use of symbolic values that differ in orders of magnitude

The Stanford how things work project

We provide an overview of the Stanford How Things Work (HTW) project, an ongoing integrated collection of research activities in the Knowledge Systems Laboratory at Stanford University. The project is developing technology for representing knowledge about engineered devices in a form that enables the knowledge to be used in multiple systems for multiple reasoning tasks and reasoning methods that enable the represented knowledge to be effectively applied to the performance of the core engineering task of simulating and analyzing device behavior. The central new capabilities currently being developed in the project are automated assistance with model formulation and with verification that a design for an electro-mechanical device satisfies its functional specification

Modeling as a fragment assembling process.

Model based reasoning about physical systems deals with diagnosis, supervision, interpretation, explanation, etc. Most of the contributions to this domain do not pay much attention to model construction, and it was generally accepted that a model was available or could be easily obtained. This assumption is no more valid when we tackle real industrial problems rather than toy examples. The situation is even worst since there is no standard methodology or approach to making models. In this paper we provide a framework to elaborate models which are suitable for model based reasoning in general, and for fault diagnosis in particular. The framework relies on the bond graphs notation, which allows a uniform approach for the different physical domains and offers compositional view of the system

Compositional Model Conversion

This dissertation presents an initial work towards the development of a technique to convert compositional models from one modelling paradigm to another, by means of a pair of equivalent compositional modelling domain theories. The mapping between model fragments of the two domain theories is not necessarily in a one-to-one manner. It might be the case that a model fragment in one domain theory covers parts of several model fragments in the other domain theory. This is one of the major conversion problems that this technique will focus on. The compositional modelling of ecological systems is used as a testing domain for the implemented conversion technique. For this work, system dynamics and object-oriented representations are the two modelling paradigms adopted. The major intention of this conversion application, implemented in the C++ programming language, is to convert a system dynamics model, composed through a compositional modelling technique, to an object-oriented model. The resulting object-oriented model is expected to reflect the same scenario, but with a different representation, compared to the model produced within the system dynamics modelling paradigm

Compositional model repositories via dynamic constraint satisfaction with order-of-magnitude preferences

The predominant knowledge-based approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the constituent interacting components of a system and translates it into a useful mathematical model. This paper presents a novel compositional modelling approach aimed at building model repositories. It furthers the field in two respects. Firstly, it expands the application domain of compositional modelling to systems that can not be easily described in terms of interacting functional components, such as ecological systems. Secondly, it enables the incorporation of user preferences into the model selection process. These features are achieved by casting the compositional modelling problem as an activity-based dynamic preference constraint satisfaction problem, where the dynamic constraints describe the restrictions imposed over the composition of partial models and the preferences correspond to those of the user of the automated modeller. In addition, the preference levels are represented through the use of symbolic values that differ in orders of magnitude