Integrated Diagnostics and Artificial Intelligence

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.

Detail:

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

Availability:
Format	Date	Length	Location	Price
ConsulTraining				RFQ
CourseNotes				RFQ
Onsite				RFQ
Private			Los Angeles	RFQ