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In typical life data analysis, the practitioner analyzes life data from a sampling of units operated under normal conditions. This analysis allows the practitioner to quantify the life characteristics of the product and make general predictions about all of the products in the population. For a variety of reasons, engineers may wish to obtain reliability results for their products more quickly than they can with data obtained under normal operating conditions. As an alternative, these engineers may use quantitative accelerated life tests to capture life data under accelerated stress conditions that will cause the products to fail more quickly without introducing unrealistic failure mechanisms.
This document presents an overview of basic concepts in quantitative accelerated life testing data analysis and some suggestions for additional research. ReliaSoft’s ALTA software provides a complete array of accelerated life data analysis tools.
Accelerated testing methods can be either qualitative or quantitative. Qualitative accelerated tests (such as HALT, HAST, torture tests or “shake & bake” tests) are used primarily to reveal probable failure modes for the product so that product engineers can improve the product design. Quantitative accelerated life tests (QALT) are designed to produce the data required for accelerated life data analysis. This analysis method uses life data obtained under accelerated conditions to extrapolate an estimated probability density function (pdf) for the product under normal use conditions.
QALT tests can employ usage rate acceleration or overstress acceleration to speed up the time-to-failure for the units under test. With usage rate acceleration, which is appropriate for products that do not operate continuously under normal conditions, the analyst operates the units under test at a greater rate than normal to simulate longer periods of operation under normal conditions. Data from this type of test can be analyzed with standard life data analysis techniques. With overstress acceleration, one or more environmental factors that cause the product to fail under normal conditions (such as temperature, voltage, humidity, etc.) are increased in order to stimulate the product to fail more quickly. Data from this type of test require special accelerated life data analysis techniques that include a mathematical model to “translate” the overstress pdfs to normal use conditions.
In an effective quantitative accelerated life test using overstress acceleration, the practitioner chooses one or more stress types that cause the product to fail under normal use conditions. Stress types can include temperature, voltage, humidity, vibration or any other stress that directly affects the life of the product. He/she applies the stress(es) at carefully selected increased levels and then records the times-to-failure for the products under accelerated test conditions. For example, if a product normally operates at 290K and high temperatures cause the product to fail more quickly, then the accelerated life test for the product may involve testing the product at 310K, 320K and 330K in order to stimulate the units under test to fail more quickly. In this example, the stress type is temperature and the accelerated stress levels are 310K, 320K and 330K. The use stress level is 290K. [View a brief introduction to common stress loading schemes and stress profiles in accelerated life testing.]
Using the life data obtained at each accelerated stress level, the analyst can use standard life data analysis techniques to estimate the parameters for the lifetime distribution that best fits the data at each stress level (e.g., Weibull, exponential or lognormal). This results in an overstress pdf for each accelerated stress level. Another mathematical model, the life-stress relationship, is then required to estimate the pdf at the normal use stress level based on the characteristics of the pdfs at each accelerated stress level.
Statisticians, mathematicians and engineers have developed life-stress relationship models that allow the analyst to extrapolate a use level probability density function from life data obtained at increased stress levels. These models describe the path of a particular life characteristic of the distribution from one stress level to another. The life characteristic can be any life measure expressed as a function of stress. For example, for the Weibull distribution, the scale parameter (eta) is considered to be stress-dependent. Therefore, the life-stress model for data that fits the Weibull distribution is assigned to eta.
The practitioner must choose a life-stress relationship that fits the type of data being analyzed. Available life-stress relationships include the Arrhenius, Eyring and inverse power law models. These models are designed to analyze data with one stress type (e.g., temperature, humidity or voltage).
The practitioner must choose a life-stress relationship that fits the type of data being analyzed. Available life-stress relationships include the Arrhenius, Eyring and inverse power law models. These models are designed to analyze data with one stress type (e.g., temperature, humidity or voltage).
The general log-linear and proportional hazards models can be used to analyze data where up to eight stress types need to be considered.
Finally, the cumulative damage (or cumulative exposure) model has been developed to analyze data where the application of the stress (either at the accelerated stress levels or at the use stress level) varies with time.
Once you have calculated the parameters to fit a life distribution and a life-stress relationship to a particular data set, you can obtain the same plots and calculated results that are available from standard life data analysis. Some additional results, related to the effects of stress on product life, are also available. Some frequently used metrics include:
