 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
Educational
Courses and Resources
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