Accelerated life testing (ALT) refers to the application of higher stresses to a product, such as temperature, humidity, and/or vibrations, so that it fails more quickly than it would during normal use. For a new product design, ALT involves applying stresses above what the product is likely to experience in the field to identify and resolve component weaknesses early in the design process. Typically used to analyze highly reliable products, ALT uses life stress models to extrapolate the test data from a small sample of overstressed products to predict the reliability of the product at normal stress levels.

Thus, ALT provides for obtaining accurate reliability estimates in shorter time frames.

You supply the data points collected during ALT for analysis. To find the distribution that best fits a data set, you use best-fit distribution analysis, which estimates parameter values for the Weibull, lognormal, and exponential failure distributions. The distribution with the best fit provides you with clues about the population from which the test data is drawn. The life stress model extrapolates the data from this small sample of overstressed products to predict the reliability of the product at normal stress levels. Once the data set is calculated, you can view various plots and generate reports to accurately determine product reliability in the field.