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Accelerated Tests as an Effective Means of Quality Improvement

Accelerated testing is an effective tool for predicting when systems fail, where the system can be as simple as an engine gasket or as complex as a magnetic resonance imaging (MRI) scanner. In particular, you might conduct an experiment to determine how factors such as temperature and voltage impose enough stress on the system to cause failure. Because system components usually meet nominal quality standards, it can take a long time to obtain failure data under normal-use conditions. An effective strategy is to accelerate the experiment by testing under abnormally stressful conditions, such as higher temperatures. Following that approach, you obtain data more quickly, and you can then analyze the data by using the RELIABILITY procedure in SAS/QC ® . The analysis is a three-step process: you establish a probability model, explore the relationship between stress and failure, and then extrapolate to normal-use conditions. Graphs are a key component of all three stages: you choose a model by comparing residual plots from candidate models, use graphs to examine the stress-failure relationship, and then use an appropriately scaled graph to extrapolate along a straight line. This paper guides you through the process, and it highlights features added to the RELIABILITY procedure in SAS/QC 13.1.