Fault diagnosis of machines

Diagnosing multiple faults

Abstract: Diagnostic tasks require determining the difierences between a model of an artifact and the artifact itself. The difierences between the manifested behavior of the artifact and the predicted behavior of the model guide the search for the difierences between the artifact and its model. The diagnostic procedure presented in this paper is model-based, inferring the behavior of the composite device from knowledge of the structure and function of the individual components comprising the device. The system (GDE | General Diagnostic Engine) has been implemented and tested on many examples in the domain of troubleshooting digital circuits. This research makes several novel contributions: First, the system diagnoses failures due to multiple faults. Second, failure candidates are represented and manipulated in terms of minimal sets of violated assumptions, resulting in an e‐cient diagnostic procedure. Third, the diagnostic procedure is incremental, exploiting the iterative nature of diagnosis. Fourth, a clear separation is drawn between diagnosis and behavior prediction, resulting in a domain (and inference procedure) independent diagnostic procedure. Fifth, GDE combines modelbased prediction with sequential diagnosis to propose measurements to localize the faults. The normally required conditional probabilities are computed from the structure of the device and models of its components. This capability results from a novel way of incorporating probabilities and information theory into the context mechanism provided by AssumptionBased Truth Maintenance.

Qualitative process theory

Abstract: Objects move, collide, flow, bend, heat up, cool down, stretch, compress, and boil. These and other things that cause changes in objects over time are intuitively characterized as processes . To understand commonsense physical reasoning and make programs that interact with the physical world as well as people do we must understand qualitative reasoning about processes, when they will occur, their effects, and when they will stop. Qualitative process theory defines a simple notion of physical process that appears useful as a language in which to write dynamical theories. Reasoning about processes also motivates a new qualitative representation for quantity in terms of inequalities, called the quantity space . This paper describes the basic concepts of qualitative process theory, several different kinds of reasoning that can be performed with them, and discusses its implications for causal reasoning. Several extended examples illustrate the utility of the theory, including figuring out that a boiler can blow up, that an oscillator with friction will eventually stop, and how to say that you can pull with a string, but not push with it.

An assumption-based TMS

Abstract: This paper presents a new view of problem solving motivated by a new kind of truth maintenance system. Unlike previous truth maintenance systems which were based on manipulating justifications, this truth maintenance system is, in addition, based on manipulating assumption sets. As a consequence it is possible to work effectively and efficiently with inconsistent information, context switching is free, and most backtracking (and all retraction) is avoided. These capabilities motivate a different kind of problem-solving architecture in which multiple potential solutions are explored simultaneously. This architecture is particularly well-suited for tasks where a reasonable fraction of the potential solutions must be explored.

A qualitative physics based on confluences

Abstract: A qualitative physics predicts and explains the behavior of mechanisms in qualitative terms. The goals for the qualitative physics are (1) to be far simpler than the classical physics and yet retain all the important distinctions (e.g., state, oscillation, gain, momentum) without invoking the mathematics of continuously varying quantities and differential equations, (2) to produce causal accounts of physical mechanisms that are easy to understand, and (3) to provide the foundations for commonsense models for the next generation of expert systems. This paper presents a fairly encompassing account of qualitative physics. First, we discuss the general subject of naive physics and some of its methodological considerations. Second, we present a framework for modeling the generic behavior of individual components of a device based on the notions of qualitative differential equations (confluences) and qualitative state. This requires developing a qualitative version of the calculus. The modeling primitives induce two kinds of behavior, intrastate and interstate, which are governed by different laws. Third, we present algorithms for determining the behavior of a composite device from the generic behavior of its components. Fourth, we examine a theory of explanation for these predictions based on logical proof. Fifth, we introduce causality as an ontological commitment for explaining how devices behave.