Diagnostic reasoning based on structure and behavior

Abstract We describe a system that reasons from first principles, i.e., using knowledge of structure and behavior. The system has been implemented and tested on several examples in the domain of troubleshooting digital electronic circuits. We give an example of the system in operation, illustrating that this approach provides several advantages, including a significant degree of device independence, the ability to constrain the hypotheses it considers at the outset, yet deal with a progressively wider range of problems, and the ability to deal with situations that are novel in the sense that their outward manifestations may not have been encountered previously. As background we review our basic approach to describing structure and behavior, then explore some of the technologies used previously in troubleshooting. Difficulties encountered there lead us to a number of new contributions, four of which make up the central focus of this paper. • — We describe a technique we call constraint suspension that provides a powerful tool for troubleshooting. • — We point out the importance of making explicit the assumptions underlying reasoning and describe a technique that helps enumerate assumptions methodically. • — The result is an overall strategy for troubleshooting based on the progressive relaxation of underlying assumptions. The system can focus its efforts initially, yet will methodically expand its focus to include a broad range of faults. • — Finally, abstracting from our examples, we find that the concept of adjacency proves to be useful in understanding why some faults are especially difficult to diagnose and why multiple representations are useful.

Diagnostic Reasoning Based on Structure and Behavior

Modeling digital circuits for troubleshooting

The use of knowledge engineering in diagnostic systems, is aimed primarily at exploiting procedural knowledge (about: systems operations, configuration, observations, calibration, maintenance), in connection with failure detection and test generation tasks. Next, the goal is to devise knowledge representation schemes whereby the failure events can be analyzed by merging highly diverse sources of information: analog/digital signals, logical variables and test outcomes, text from verbal reports, and inspection images. The final goal is to ease the operator workload when interfacing with the system under test and/or the test equipment, or with reliability assessment software packages. The paper will present key notions, methods and tools from: knowledge representation, inference procedures, pattern analysis. This will be illustrated by reference to a number of current and potential applications for e.g.: electronics failure detection, control systems testing, analysis of intermittent failures, false alarm reduction, test generation, maintenance trainers.

Survey of Expert Systems for Fault Detection, Test Generation, and Maintenance

Interest has grown recently in developing expert systems that reason "from first principles", i.e., capable of the kind of problem solving exhibited by an engineer who can diagnose a malfunctioning device by reference to its schematics, even though he may never have seen that device before. In developing such a system for troubleshooting digital electronics, we have argued for the importance of pathways of causal interaction as a key concept. We have also suggested using a layered set of interaction paths as a way of constraining and guiding the diagnostic process.

We report here on the implementation and use of these ideas. We show how they make it possible for our system to generate a few sharply constrained hypotheses in diagnosing a bridge fault.

Abstracting from this example, we find a number of interesting general principles at work. We suggest that diagnosis can be viewed as the interaction of simulation and inference and we find that the concept of locality proves to be extremely useful in understanding why bridge faults are difficult to diagnose and why multiple representations are useful.

/pdf/diagnosis-via-causal-reasoning-paths-of-interaction-and-the-10nrye1icq.pdf

Diagnosis via causal reasoning: paths of interaction and the locality principle

The development of expert systems for troubleshooting digital electronics is considered. It is shown that suitably represented descriptions of structure and behavior set an important foundation. The authors approach offers a unity of device description and simulation, since the descriptions themselves are runnable. Unsolved problems are noted. 10 references.

Representing Structure and Behavior of Digital Hardware

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Representing Structure and Behavior of Digital Hardware