Integrated Diagnostics & Prognostics for Prediction of Aircraft Electronic System Power Supply Failures & Useful Life Remaining

Award Information
Department of Defense
Award Year:
Phase I
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Small Business Information
125 Tech Park Drive, Rochester, NY, 14623
Hubzone Owned:
Minority Owned:
Woman Owned:
Principal Investigator:
Rolf Orsagh
Manager of Monitoring Sys
(585) 424-1990
Business Contact:
Mark Redding
(585) 424-1990
Research Institute:
In response to SBIR topic N03-197, Impact Technologies, in collaboration with The Northrop Grumman Corporation and Harris Government Communications Systems (developer of electronic power supplies for the JSF), proposes to develop and demonstrate an on-board integrated diagnostic/prognostic system for assessing the useful life remaining of aircraft electronic system power supplies. Implementation of the prognostic and health management (PHM) concept requires an ability to relate equipment health state information including operational history and detection of incipient faults to accurate useful life remaining predictions at any point in the system's life cycle. The dominant failure modes of switching mode power supplies, as identified by a Pareto analysis of historical reliability data, will be addressed in the Phase I program. The proposed approach integrates collaborative diagnostic and prognostic techniques from engineering disciplines including statistical reliability modeling, damage accumulation models, physics of failure modeling, and sensor-based condition monitoring using automated reasoning algorithms. Novel features extracted from sensed parameters such as temperature, power quality, and load will be analyzed using advanced fault detection and damage accumulation algorithms. Using model-based assessments in the absence of fault indications, and updating the model-based assessments with sensed information when it becomes available provides health state awareness at any point in time. Intelligent fusion of this diagnostic information with historical component reliability statistics provides a robust health state awareness as the basis for accurate prognostic predictions. Complementary prognostic techniques including analysis of projected operating conditions by physics-based component aging models, empirical (trending) models, and system level failure progression models will be used to develop verifiable prognostic models. The proposed approach, diagnostic techniques, and prognostic models will be demonstrated through accelerated failure testing of commercially available switching mode power supplies. Only through the utilization of all of these sources of engineering information can up-to-date assessments and predictions of power supply remaining useful life be determined for use in automated PHM systems.

* information listed above is at the time of submission.

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