LuValle / LeFevre / Kannan Design and Analysis of Accelerated Tests for Mission Critical Reliability

Erscheinungsjahr 2004
ISBN: 978-1-135-43618-6
Verlag: Taylor & Francis
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 248 Seiten

Early approaches to accelerated testing were based on the assumption that there was a simple acceleration factor that would correspond to a linear scaling of time from the operating stress to the accelerating stress. This corresponds to the simplest physical model of the kinetics governing the underlying degradation, but this simple model does not always hold. We need to understand what more complex physical models may look like.

Design & Analysis of Accelerated Tests for Mission Critical Reliability presents innovative theory and methods for recognizing and handling the more complicated, cases often encountered in practice. The theory integrates a physical understanding of underlying phenomena and the statistical modeling of observation "noise" to provide a single theoretical framework for accelerated testing. The treatment includes general approaches that can be used with various computational software packages and an explicit computing environment in S-PLUS. Source code written by the authors is included and available for download from http://www.crcpress.com/e_products/downloads.

For practitioners, this book provides immediately useable tools. For researchers, it presents intriguing open questions. And for the academic community, numerous worked examples, end-of-chapter exercises, and a format that relegates technical and theoretical details to chapter appendices make this an outstanding supplementary textbook for senior and graduate-level students.

Zielgruppe

Aeropsace engineers, automotive engineers, defense engineers, space engineers, coatings and paints specialists, telecommunications and information system industry professionals, researchers in civil, mechanical, optical, and electrical industry, graduate students of industrial engineering, and applied statisticians, packaging engineers for drug and food companies,

Autoren/Hrsg.

LuValle, Michael J.

LeFevre, Bruce G.

Kannan, SirRaman

Fachgebiete

• Mathematik | Informatik Mathematik Mathematik Allgemein

Inhaltsverzeichnis

BACKGROUND
Introduction
Other Approaches
The Foundation of our Approach
An Example
The Organization of this Book
Complement to Chapter 1: Background Kinetics and Statistics
DEMARCATION MAPPING: INITIAL DESIGN OF ACCELERATED TESTS
Analytical Theory of Thermal Demarcation Maps
Designing an Acceptance Test for a Purely Thermal Process
Simple Temperature Humidity Models
Designing an Acceptance Test for a Temperature Humidity Model
Mechanical Cycling Models
Acceptance Testing for Mechanical Cycling Induced by Thermal Cycling
Computational Demarcation Mapping
Beta Binomial Interpretation of 0 Failures
An Extrapolation Theorem
Summary
Complements to Chapter 2
INTERFACE FOR BUILDING KINETIC MODELS
Descriptions and Concepts Behind the Interface
Complement to Chapter 3, Our Interface in S-PLUS®, Kinetic Data Objects and the GUIs to Create Them
EVANESCENT PROCESS MAPPING
Building Blocks for the Theory
Identifying Neighborhoods of Models, Sampling, and "Chunking"
Example
Summary, Limitations of Accelerated Testing
Complement to Chapter 4: Using the Evanescent Process Mapping Interface to duplicate example 4.3
DATA ANALYSIS FOR FAILURE TIME DATA
A Simple Data Set
Adding Physical Sense to the Model
Analysis of a Real Data Set
Complement: Maximum Likelihood Analysis
Complement: Statistical Estimation of Kinetics from Failure Time Data
Complement: Pseudo Maximum Likelihood Estimation
Complement: The Kaplan Meier Estimate
Complement: Printed Wiring Board Data
Complement: Using the Interface
Complement: Exercises to Explore some Questions in Experiment Design
DATA ANALYSIS FOR DEGRADATION DATA
Motivation and Models
Background for the Example
Data Analysis for the Example
Complement: Background Statistical Theory
Complement: Using the Software to Analyse the Example Data
Complement: Exercises for Data Analysis and Experiment Design