Payload Integrated Health Management Systems
Department of Defense
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Small Business Information
Management Sciences, Inc.
6022 Constitution Avenue NE, Albuquerque, NM, 87110
Socially and Economically Disadvantaged:
Director of Nanomaterials
Director of Nanomaterials
AbstractTaxpayers and private businesses have realized significant cost savings to system operations through applied Prognostics and Health Management (PHM) technology. PHM techniques have been used to characterize space mission cryocooler prognostics; to complete the integrated space system PHM approach, realistic performance degradation models for associated Electro-Optical (EO) payload systems are needed. The foundation for developing PHM-centric EO payload models, and bringing these models towards realization of embedded PHM hardware, rests on the techniques of Integrated Optical Modeling. By integrating optical design prescriptions, Finite Element Analysis, mechanical CAD, and data processing algorithms with state-space discrete-time simulation, dynamic analysis of EO system performance is achieved. This state-space linear representation can be extended to PHM functionality by adding degradation variables in the state model, applying filtering algorithms for diagnostics, and prediction algorithms for prognostics. The integrated modeling approach also provides means to incorporate empirical data, statistical reliability and remaining useful life analysis, state awareness sensor design, integration with larger system models, and reduction to embedded FPGA PHM hardware. BENEFIT: Our Phase I effort will produce practical EO predictive reference models and tools useful to systems designers seeking to answer space payload mission lifecycle utilization and optimization requirements. From a practical standpoint this will enable payload designers to specify possible faults and degradation interactions and to predict failures and life remaining of EO components, as well as to provide design engineering analysis products. Successful implementation and application of the EO predictive models and resulting publications will provide a foundation for further EO community collaboration on PHM applications. Commercial benefits will be the development of marketable EO analysis tools and systems house capability for EO PHM design and application services, as well as new engineering and manufacturing job opportunities.
* information listed above is at the time of submission.