Predicting In-Plant Test Failures

This paper presents an approach for prediction of in-plant test failures so that appropriate cost planning can be undertaken. The approach is based on the technique of multiple regression analysis. The primary independent variables include equipment complexity, reliability program emphasis, and test program severity. As a guideline in estimating the number of in-plant test failures, a Test Failure Estimator (TFE) graph is provided. The results of this analysis showed a high degree of correlation between the number of test failures experienced and the combined effect of equipment complexity and test program severity. Statistical tests confirmed that both the multiple correlation coefficient and the degree of explained variance are s-significant at a high probability level. Thus, this multiple regression analysis 1) verifies a s-significant correlation between the independent variables and in-plant test failures and 2) demonstrates the feasibility of using these variables to predict the number of in-plant test failures. This area still needs more study. In the meantime, the TFE graph can be used as a guideline in estimating the number of in-plant test failures.