System reliability prediction based on historical data

Abstract

AbstractThis paper describes the development and implementation of a computerized reliability prediction model at the IBM facility located in Research Triangle Park, North Carolina. Through the analysis of historical life‐test data, the model provides maximum likelihood estimates of the assumed Weibull life distributions of various types of components. The resulting component life distribution estimates are used to predict the reliability of new system configurations. This approach is based upon the well‐known theory of competing risks. Our model, however, is unique in that it allows for the analysis of a pooled set of life data, i.e. life data from different types of systems, to obtain component estimates. This feature greatly generalizes the competing risks framework and hence offers advantages over the more traditional approach. We present the model and discuss various issues that were found to be critical to its successful implementation at IBM.