Download or read book Data Fusion for Enhanced Aircraft Engine Prognostics and Health Management written by National Aeronaut Administration (Nasa) and published by . This book was released on 2020-08-03 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aircraft gas-turbine engine data is available from a variety of sources, including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data fusion is the integration of data or information from multiple sources for the achievement of improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/ information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This report describes a basic PHM data fusion architecture being developed in alignment with the NASA C-17 PHM Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center, NASA Dryden Flight Research Center, and Pratt & Whitney have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion, as it applies to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This report will provide a chronology and summary of the work accomplished under this research contract. Volponi, Al Glenn Research Center NASA/CR-2005-214055, E-15412 NAS3-01138 ARTIFICIAL INTELLIGENCE; INFORMATION SYSTEMS; PROPULSION; RELIABILITY; STATISTICAL ANALYSIS; CHRONOLOGY; FLIGHT TESTS; FUZZY SYSTEMS; GAS TURBINE ENGINES