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Integrated Diagnostics and Artificial Intelligence

Integrated Diagnostics and Artificial Intelligence
  Integrated Diagnostics and Artificial Intelligence

email Questions

 

 

 

 

What you will learn: 

How integrated diagnostics and artificial intelligence (AI) can be used to improve product support. You will learn the basic principals of artificial intelligence and how they apply to test and diagnosis. You will also learn diagnostic concepts, including dependency modeling and optimizations.

Abstract: 

The course introduces the concepts of optimization, pattern recognition and inferences as they apply to diagnostics. The Integrated Diagnostics curriculum examines the design of a system and helps assess to what degree the design can be supported. In addition to testability, other considerations, such as maintainability, reliability and even documentation are critical parts of an Integrated Diagnostics approach.

Who should attend: 

All test engineering professionals should attend, but anyone concerned about the support of products will find this course valuable.

COURSE OUTLINE:

Introduction to Artificial Intelligence

Dependency Modeling

Optimization

Searches

  • Depth-First Search
  • Breadth-First Search

Inference

  • Learning
  • Prepositional Logic
  • Rules
  • First-Order Logic
  • Knowledge Representation

Diagnostics

  • Path Sensitization
  • Heuristic Search - Fault Trees
  • A*-Algorithm

AI-ESTATE

Basic Concepts of Integrated Diagnostics

A Model Based Approach

  • Optimization Diagnosis
  • Modeling Process

Dependency Modeling

  • WSTA, STAT, STAMP and other modelers

A Sample Problem

Advanced Topics

  • Multi-Criterion Optimization
  • Diagnosis with Imperfect Information
  • Adaptation and Learning in Diagnosis

Activities in Integrated Diagnostics

  • Applications
  • Tools
  • Environments
  • ATE Initiatives

Future of Integrated Diagnostics

See our complete selection of
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