Data Analytic Framework Supporting Diagnostics/Prognostics and Health Management

A solid data analytic framework is the backbone for successful diagnostics/prognostics and health management (PHM) capabilities resulting in safe, repeatable and accurate outcomes. This framework allows for increased rigor and extensibility across the product lifecycle (engineering development, integration and validation, manufacturing, and sustainment). The framework fits "hand in glove" with optimizing Reliability and Maintainability (R&M) design and analyses to find and fix issues early in product design and decrease sustainment cost over the long term. The data analytic framework involves a defined data architecture (data parameters, data interfaces, data processing), data correlations, and data interpreting/reporting results with actionable summaries driving reliability improvements and enabling maintenance success. The paper breaks down the data analytics types (diagnostic, predictive and prescriptive) and walks through how these drive different capabilities and value streams in reliability and maintainability use cases. Gap areas, challenges, and risks are discussed to identify areas that may need closer attention. In summary, this data analytic framework walks through the premises for developing effective diagnostic/PHM solutions that determine actionable information necessary for safe, repeatable and accurate outcomes.