A general architecture for factory-based diagnosis of electronics

ABSTRACT

This paper describes an architecture, IFADS (Integrated Factory-based Automatic Diagnostic System), that is well suited for performing diagnosis of electronic assemblies that fail factory-based quality assurance testing. Diagnostics in a manufacturing setting differ in several ways from that in an end-user setting. These differences are noted and the motivation for the IFADS design is shown. The purpose of modern Automatic Test Equipment (ATE) is to indicate whether a piece of equipment is operating within specifications. “Diagnosis”, on the other hand goes beyond this to identify the cause of the problem as well. ATE usually contains most of the functionality required to extend this to diagnosis, e.g., the ability to stimulate the inputs in a variety of ways and to measure the outputs and testpoints. Lacking is the ability to analyze the data and determine the probable cause of the fault or the ability to control the tests applied based on the results of previous tests. One of the most interesting characteristics of the IFADS architecture is that it fuses several reasoning techniques to arrive at a diagnosis. Some of these are topological methods, including backtracing from “bad” testpoints and backtracing from “good” Testpoints. A new topological technique is presented which is able to assemble a first approximation of an ordered list of the device's components such that components earlier in the list are more likely to be the cause of the problem than components later in the list. Other techniques use causal information to explain abnormal testpoint data. Still other methods are heuristically based. (Our goal is to minimize the amount of heuristic knowledge required and, for generality, evolve our system toward one which can reason from circuit information alone.) Each of these techniques is fully discussed. In order to make full use of these reasoning methods, the IFADS architecture utilizes several types of domain knowledge. For a particular piece of equipment the following information is represented: the physical hierarchy (how the equipment is assembled), the functional hierarchy (how the equipment is broken down according to function — usually not the same as the physical hierarchy), a mapping between the physical and functional hierarchies, function names and cause-effect relations, and statistical failure probabilities. We built a working prototype of the IFADS architecture to diagnose a torpedo sonar receiver.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.4 Knowledge Representation Formalisms and Methods
          Subjects: Representations (procedural and rule-based)

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving
          Subjects: Deduction (e.g., natural, rule-based)

  J. Computer Applications
  J.1 ADMINISTRATIVE DATA PROCESSING
      Subjects: Manufacturing
  J.7 COMPUTERS IN OTHER SYSTEMS
      Subjects: Command and control

General Terms:
Design, Performance