Intelligent maintenance advisor for turbine engines

Current gas turbine diagnostic and condition monitoring systems generate information that, while accurate the majority of time, is produced without regard to (or access to in most cases) other sources of related information. This leads to ambiguity in troubleshooting, requires maintenance personnel to make uninformed decisions and results in erroneous component removals and This paper describes a program, to address this problem through the design and test of a condition-based Intelligent Maintenance Advisor for Turbine Engines (IMATE) system. This system integrates sensor and model data from various diagnostic, prognostic and usage sources with information fusion algorithms to assess engine condition. A more focused maintenance recommendation can then be made based upon all available information rather than several independent and potentially conflicting sources. The program demonstrates a dual use capability by conducting demonstration testing on both a commercial and a military engine