With the development of science and technology, products have a longer life and higher reliability. However, for reliability engineers and technicians, it becomes more difficult to obtain product life data under the environmental stress conditions of normal operation of the product, and therefore it is impossible to evaluate the life and reliability of the product. In order to solve this problem, highly accelerated life test technology came into being. Accelerated life test refers to the method of increasing the stress to promote the failure of the sample in a short period of time to predict the reliability under normal working conditions or storage conditions, but does not change the failure distribution of the tested sample.
The unified definition of accelerated life test was first proposed by the Merome Airshow Center in 1967. Accelerated life test is based on reasonable engineering and statistical assumptions, and uses statistical models related to physical failure laws to accelerate acceleration beyond normal stress levels. The information obtained under the environment is converted to obtain a reproducible numerical estimation of the characteristics of the product under the rated stress level. In short, the accelerated life test is a life test method that shortens the test cycle by increasing the test stress while keeping the failure mechanism unchanged. The accelerated life test adopts the accelerated stress level to carry out the life test of the product, thereby shortening the test time, improving the test efficiency, and reducing the test cost.
A series of parameters must be determined for accelerated life test, including (but not limited to): test duration, sample size, test purpose, required confidence level, required accuracy, cost, acceleration factor, field environment, test environment, acceleration factor Calculation, Weibull distribution slope or β parameter. The key to determining product life with accelerated life test methods is to determine the acceleration factor, which is sometimes the most difficult.
(1) Physical acceleration model
The physical acceleration model is based on the physical and chemical explanation of the product failure process. A typical physical acceleration model is the Arrhennis model, which describes the relationship between product life and temperature stress. Another typical physical acceleration model is the Eyring model, which is based on the theory of quantum mechanics. The model also describes the relationship between product life and temperature stress.
(2) Experience acceleration model
The empirical acceleration model is proposed based on the engineers' long-term observations of product performance. Typical empirical acceleration models such as the inverse power law model and the Coffin-Manson model. The inverse power law model describes, for example, the relationship between voltage or pressure stress and product life. The Coffin-Manson model gives the relationship between temperature cycling stress and product life.
(3) Statistical acceleration model
Statistical acceleration models are based on statistical analysis methods, and are often used to analyze data that are difficult to explain by physical and chemical methods. Statistical acceleration models can be divided into parametric models and non-parametric models. The number and characteristics of the parameters in the parametric model are determined, while the number and characteristics of the parameters in the non-parametric model are flexible and do not need to be determined in advance. The parametric model needs to determine the life distribution form of the product in advance, and the non-parametric model is a model with no distribution assumption, which is more favored by researchers.