Accelerated Testing Terms
Accelerated life testing
A testing strategy whereby the engineer extrapolates a product’s failure behavior at normal conditions from life data obtained at accelerated stress levels. Since products fail more quickly at higher stress levels, this sort of strategy allows the engineer to obtain reliability information about a product (e.g., mean life, probability of failure at a specific time, etc.) in a shorter time.
The ratio of the product’s life at the use stress level to its life at an accelerated stress level. For example, if the product has a life of 100 hours at the use stress level, and it is being tested at an accelerated stress level which reduces its life to 50 hours, then the acceleration factor is 2.
An accelerated life testing model used in accelerated life testing to establish a relationship between absolute temperature and reliability. It was originally developed by Swedish chemist Svante Arrhenius to define the relationship between temperature and the rates of chemical reaction.
General log-linear model
An accelerated life testing model that can account for multiple non-thermal stresses as acceleration factors. In ALTA PRO, this model allows the user to select a life-stress relationship (Arrhenius, Inverse Power Law or Exponential) for each stress.
"HALT" stands for "Highly accelerated life testing." It is an accelerated testing method used primarily to reveal probable failure modes for the product.
"HASS" stands for "Highly accelerated stress screening." It is similar to the HALT testing method, except it is applied during the production stage to prevent the shipment of defective items.
A relationship that describes how stress levels affect the reliability of a product. Various mathematical models (e.g., the Arrhenius model) are available to describe a product's life-stress relationship.
Proportional hazards model
An accelerated life testing model that can account for multiple non-thermal stresses as acceleration factors. This model allows the use of zero as a stress value, which enables the analysis of data with indicator variables (e.g., 0 = on/off and 1 = continuous operation).
A testing strategy whereby units are tested at stresses higher than what would be encountered during normal operating conditions, usually to induce failures.