In industry, bogey testing, also known as the zero-failure testing, is often used to demonstrate product reliability. This test method is simple to apply; however, it requires excessive test time and/or a large sample size, and thus is usually unaffordable. For some products whose failure is defined as a performance characteristic exceeding a threshold, it is possible to measure the performance characteristic during testing. The measurement data can be employed to predict whether or not a test unit will fail by the end of test. When there are sufficient data to make such a prediction with a high degree of confidence, the test of the unit can be terminated. As a result, the test time is reduced.
This presentation describes the test method, sample size computation, degradation models, and cost function for the lognormal bogey testing. Then the presentation discusses the optimum test plans, which choose the optimal sample size and the expected test time by minimizing the total test cost and simultaneously satisfying the constraints on the type II error and the available sample size. An example is given to illustrate the test method.