This test is designed with the assumption that product reliability is a combination of its individual reliabilities under various operational and environmental stresses and of the undetermined interaction among these stresses. A well-designed reliability test accounts for all cumulative operational and environmental exposures to the stresses that the product will encounter in field use. To determine the level and duration of each of the individual stresses to be applied in the test, it is assumed that the stresses are independent, and a separate factor is added to make up for any unknown stress interactions. The synergism or the test sequence is not disregarded, as it might be the factor contributing to lower demonstrated reliability. The individual stresses are accelerated to allow for reasonable and cost-effective test duration. This test design cannot be accomplished without detailed knowledge of the product's usage profile, sequence of operation, and expected use environments.
The choice of test data analysis technique will depend on whether there were any test failures and on the type of failures. Where failures occur, acceleration factors are applied to determine the corresponding times to failure in field use. The test may be conducted as a reliability growth or a fixed duration test by applying the appropriate analytical techniques. The reliability growth technique may be preferred, as it normally yields tests of shorter duration.
Accelerated reliability testing provides realistic lifetime reliability demonstration or validation and, if performed early enough, allows for design changes to improve product reliability.
In designing any accelerated life test, caution should be exercised to avoid exceeding the environmental rating of the tested product, as high test acceleration can also induce failures that would normally not be expected to occur in the field life of the product. Also, acceleration factors must be carefully determined, preferably through accelerated testing to the failure mode of interest. Acceleration factors (their constants) may be different for different components of the tested products, thus care must be taken in their selection.
Quantitative Prediction of Perovskite Stability Using Accelerated Testing and Machine Learning