Constructing the "best" reliability data for the job

Industries, consortia, and vendors across many areas have spent decades collecting, analyzing, and tabulating fielded item and component reliability performance in terms of observed failures and operational use. This data resource provides a huge compendium of information for potential use, but can also be compartmented by industry, difficult to find out about, access, or manipulate. A method is presented that incorporates processes for reviewing these existing data sources and identifying the available information based on similar equipment, then using that generic data to derive an analog composite for analysis purposes. Once developed, this composite analog data provides a "better" representation of the reliability of the equipment or component. It can be used to support early risk or reliability trade studies, or analytical models to establish the predicted reliability data points. It also establishes a baseline that may be updated with test data or observed operational constraints and failures, i.e., using Bayesian techniques. This paper provides a descriptive compilation of historical data sources across numerous industries and disciplines, along with examples of contents and data characteristics. It then presents methods for combining failure information from different sources and mathematical use of this data in early reliability estimation and analyses. A discussion is presented of the uncertainty in the data and how that uncertainty may be used in a comparative manner to help in the selection of the "best" information for further use.