The surge in the introduction of new products coupled with the significant reduction in time from product design to manufacturing, as well as the ever increasing customer's expectation for high reliability and longer warranties have prompted industry to shorten its product test duration. In many cases, the testing of the products prototypes can take months and even years before actual production takes place. In many situations, accelerated life testing which subjects the product samples to extreme conditions might be the only viable approach to assess whether the product meets the expected long term reliability requirements. As a result, the accuracy of reliability estimates from such testing has a profound effect on the subsequent decisions regarding system configuration, warranties and preventive maintenance schedules. Inappropriate implementation of the test might cause delays in product release, termination of the entire product or catastrophic failures in the actual use of the product. In a variety of industrial applications, there could be many choices in stress loadings when conducting testing. Each stress loading has some advantages and drawbacks. This has raised many practical questions regarding the equivalence among various accelerating test plans involving different stress loadings. The problem becomes more difficult when multiple stresses are involved. To overcome these challenges, we propose to investigate and contribute fundamental and theoretical models and provide several statistical tools to facilitate practical reliability tests. New framework for planning accelerated testing under various stress loadings, especially under complex stress and multiple stresses conditions, will be established.

If successful, the research results will fill a major gap in reliability assessment needed by industry. Especially, the results will enable industry to design efficient and economical test plans, yet the results of the test will provide accurate reliability estimates. The tools to be developed will facilitate and further promote the implementation of accelerated life testing in modern industry.

Project Start
Project End
Budget Start
2006-08-01
Budget End
2010-07-31
Support Year
Fiscal Year
2006
Total Cost
$287,222
Indirect Cost
Name
Rutgers University
Department
Type
DUNS #
City
New Brunswick
State
NJ
Country
United States
Zip Code
08901