SBIR-STTR Award

Due to the complexity and the large amount of data involved in future space missions, greater autonomy is becoming necessary for mission operations, training, and vehicle health management. GeoControl Systems, Inc. (GCS) proposes to develop a fuzzy logic intelligent diagnostic system (FLIDS) to perform data reduction, data analysis, and fault diagnosis to achieve greater autonomy for various mission operation applications. The FLIDS system contains a data filter and an inference engine. The data filter is designed to intelligently select only the necessary data for analysis, while the inference engine is designed for failure detection, warning, and decision on corrective actions, based on fuzzy logic synthesis and statistical analysis. Due to its adaptive nature and on- line learning ability, the FLIDS system is capable of dealing with environmental noise, uncertainties, conflict information, and sensor faults. It can be easily incorporated in or interfaced with existing mission operation control software. Phase I effort will be system design, software development and verification on existing testbeds. The FLIDS system can be used for mission operations, astronaut training systems, integrated vehicle health management, and similar systems. It has the potential to become a standard subsystem of future spacecraft.

Keywords:
Phase_I, NASA, Abstract, FY94

___(NOTE: Note: no official Abstract exists of this Phase II projects. Abstract is modified by idi from relevant Phase I data. The specific Phase II work statement and objectives may differ)___ Due to the complexity and the large amount of data involved in future space missions, greater autonomy is becoming necessary for mission operations, training, and vehicle health management. GeoControl Systems, Inc. (GCS) proposes to develop a fuzzy logic intelligent diagnostic system (FLIDS) to perform data reduction, data analysis, and fault diagnosis to achieve greater autonomy for various mission operation applications. The FLIDS system contains a data filter and an inference engine. The data filter is designed to intelligently select only the necessary data for analysis, while the inference engine is designed for failure detection, warning, and decision on corrective actions, based on fuzzy logic synthesis and statistical analysis. Due to its adaptive nature and on- line learning ability, the FLIDS system is capable of dealing with environmental noise, uncertainties, conflict information, and sensor faults. It can be easily incorporated in or interfaced with existing mission operation control software. Phase I effort will be system design, software development and verification on existing testbeds. The FLIDS system can be used for mission operations, astronaut training systems, integrated vehicle health management, and similar systems. It has the potential to become a standard subsystem of future spacecraft.

Keywords:
Phase_I, NASA, Abstract, FY94