Results 1 
8 of
8
Process Monitoring and Diagnosis: A ModelBased Approach.
 IEEE EXPERT
, 1991
"... This article describes a method for monitoring and diagnosis of process systems based on three foundational technologies: semiquantitative simulation, measurement interpretation (tracking), and modelbased diagnosis. Compared to existing methods based on fixedthreshold alarms, fault dictionaries, ..."
Abstract

Cited by 22 (6 self)
 Add to MetaCart
This article describes a method for monitoring and diagnosis of process systems based on three foundational technologies: semiquantitative simulation, measurement interpretation (tracking), and modelbased diagnosis. Compared to existing methods based on fixedthreshold alarms, fault dictionaries, decision trees, and expert systems, several advantages accrue: ffl the physical system is represented in a semiquantitative model which, unlike a pure numeric model, predicts all possible behaviors that are consistent with the incomplete/imprecise knowledge of the system's devices and processes, ensuring, for example, that a hazardousbutinfrequent behavior will not be overlooked; ffl imprecise knowledge of parameter values and functional relationships (both linear and nonlinear) can be expressed in the semiquantitative model and used during simulation, producing a valid range for each variable; ffl incremental simulation of the model in step with incoming sensor readings, with subseq...
Qualitative Simulation: Then and Now
, 1993
"... ion, Soundness, and Incompleteness Once the abstraction relations from ODEs to QDEs, and from continuously differentiable functions to qualitative behaviors, are carefully defined 1 , the mathematical results are relatively straightforward. We can view an ordinary differential equation solver as ..."
Abstract

Cited by 19 (1 self)
 Add to MetaCart
ion, Soundness, and Incompleteness Once the abstraction relations from ODEs to QDEs, and from continuously differentiable functions to qualitative behaviors, are carefully defined 1 , the mathematical results are relatively straightforward. We can view an ordinary differential equation solver as a theoremprover for theorems of a special form: DiffEqs ` ODE State(t 0 ) ! Beh: (1) A qualitative simulation algorithm can also be viewed as a specialpurpose theoremprover: QSIM ` QDE QState(t 0 ) ! or(QBeh 1 ; : : : QBeh n ): (2) The soundness theorem says that when QSIM proves a theorem of form (2), it is true: that is, for any ODE described by the QDE, and State(t 0 ) described by QState(t 0 ), the solution Beh to the ODE is described by one of the qualitative behaviors, QBeh 1 ; : : : QBeh n . The constraint filtering algorithm makes the proof very simple: all possible real transitions from one qualitative state to the next are proposed, and only impossible ones are filtered out...
Static and Dynamic Abstraction Solves the Problem of Chatter in Qualitative Simulation
, 1997
"... One of the major factors hindering the use of qualitative simulation techniques to reason about the behavior of complex dynamical systems is intractable branching due to a phenomenon called chatter. This paper presents two general abstraction techniques that solve the problem of chatter. Eliminating ..."
Abstract

Cited by 14 (7 self)
 Add to MetaCart
One of the major factors hindering the use of qualitative simulation techniques to reason about the behavior of complex dynamical systems is intractable branching due to a phenomenon called chatter. This paper presents two general abstraction techniques that solve the problem of chatter. Eliminating the problem of chatter significantly extends the range of models that can be tractably simulated using qualitative simulation. Chatter occurs when a variable's direction of change is constrained only by continuity within a region of the state space. This results in intractable, potentially infinite branching within the behavioral description due to irrelevant distinctions in the direction of change. While a number of techniques have been proposed to eliminate chatter, none of them provide a general solution that can eliminate all instances of chatter. Chatter box abstraction and dynamic chatter abstraction provide two such solutions to this problem. Both solutions eliminate chatter by abs...
Solving Complexity and Ambiguity Problems within Qualitative Simulation
, 1997
"... vii List of Tables xiv List of Figures xv Chapter 1 Introduction 1 1.1 Reducing the Complexity of a Simulation . . . . . . . . . . . . . . . 4 1.1.1 Model Decomposition and Simulation (DecSIM) . . . . . . . 8 1.1.2 Chatter abstraction techniques . . . . . . . . . . . . . . . . . 8 1.2 Focusing and ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
(Show Context)
vii List of Tables xiv List of Figures xv Chapter 1 Introduction 1 1.1 Reducing the Complexity of a Simulation . . . . . . . . . . . . . . . 4 1.1.1 Model Decomposition and Simulation (DecSIM) . . . . . . . 8 1.1.2 Chatter abstraction techniques . . . . . . . . . . . . . . . . . 8 1.2 Focusing and Refining Ambiguous Behavioral Descriptions . . . . . . 10 1.2.1 Temporally Constrained QSIM (TeQSIM) . . . . . . . . . . . 10 1.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.4 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Chapter 2 Qualitative Simulation 14 2.1 Qualitative models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2 Qualitative states . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3 Behavioral description . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.4 Qualitative constraints . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.5 Dynamic simulation . . . . . . . . ...
SemiQuantitative Physics Compiler: where are we now and where are we going?
, 1994
"... Incomplete information is present in many engineering domains, hindering traditional and nontraditional simulation techniques. This paper describes SQPC (semiquantitative physics compiler), an implemented approach to modelling and simulation that can predict the behavior of incompletely specifie ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Incomplete information is present in many engineering domains, hindering traditional and nontraditional simulation techniques. This paper describes SQPC (semiquantitative physics compiler), an implemented approach to modelling and simulation that can predict the behavior of incompletely specified systems, such as those that arise in the water control domain. SQPC is the first system that unifies compositional modeling techniques with semiquantitative representations. We describe SQPC's foundations, QSIM and QPC, and how it extends them. We demonstrate SQPC using several examples from the water supply domain.
Design, Prototype Implementation and Experimental Evaluation of a Scalable Multiprocessor Architecture for Qualitative Simulation
, 1996
"... This dissertation presents the design, the prototype implementation and the experimental evaluation of a scalable multiprocessor for qualitative simulation. The main objective of this work is to improve the running time of the qualitative simulator QSim. In qualitative simulation, physical systems a ..."
Abstract
 Add to MetaCart
This dissertation presents the design, the prototype implementation and the experimental evaluation of a scalable multiprocessor for qualitative simulation. The main objective of this work is to improve the running time of the qualitative simulator QSim. In qualitative simulation, physical systems are modeled on a higher level of abstraction than in other simulation paradigms, like in continuous simulation. A major strength of qualitative simulation is that it can represent and reason with incomplete knowledge  qualitative simulation requires neither a complete structural description nor a fully specified initial state. All physically possible behaviors consistent with this incomplete description are predicted by qualitative simulation. In engineering, qualitative simulation is mainly applied in monitoring and diagnosis. QSim is the most prominent algorithm for qualitative simulation. QSim is implemented in Lisp and executed on generalpurpose computers. A drawback of current QSim...
Modeling Physical Systems Qualitatively
"... ■ We examine different formalisms for modeling qualitatively physical systems and their associated inferential processes that allow us to derive qualitative predictions from the models. We highlight the mathematical aspects of these processes along with their potential and limitations. The article ..."
Abstract
 Add to MetaCart
(Show Context)
■ We examine different formalisms for modeling qualitatively physical systems and their associated inferential processes that allow us to derive qualitative predictions from the models. We highlight the mathematical aspects of these processes along with their potential and limitations. The article then bridges to quantitative modeling, highlighting the benefits of qualitative reasoning–based approaches in the framework of system identification, and discusses open research issues.
Physical
"... Qualitative reasoning about physical systems has become one of the most active and productive areas in AI in recent years. While there are many different kinds of qualitative reasoning, the central role is played by qualitative simulation: prediction of the possible behaviors consistent with incomp ..."
Abstract
 Add to MetaCart
(Show Context)
Qualitative reasoning about physical systems has become one of the most active and productive areas in AI in recent years. While there are many different kinds of qualitative reasoning, the central role is played by qualitative simulation: prediction of the possible behaviors consistent with incomplete knowledge of the structure of physical system. In the retrospective [8] on my 1984 paper, &quot;Commonsense reasoning about causality: deriving behavior from structure&quot;, I describe the framework for qualitative reasoning that has motivated this work, and the applications that have come out of that framework. That paper [5] includes the conjecture that the structural and behavioral representations for qualitative simulation could be rigorously shown to be abstractions of ordinary differential equations and their solutions. My 1986 paper, &quot;Qualitative simulation&quot;, established that conjecture and legitimized the term qualitative differential equation or QDE. It also presented the clear and efficient QSIM algorithm. In this retrospective, I describe aspects of the body of work on qualitative simulation that has developed from there. 1. Background Three motivating insights led to the development of the QSIM algorithm. First, the design for the QDE representation for qualitative models, presented