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Hybrid Model Fusion for Gas Turbine Engine Diagnostics and Prognostics

Award Information

National Aeronautics and Space Administration
Award ID:
Program Year/Program:
2002 / SBIR
Agency Tracking Number:
Solicitation Year:
Solicitation Topic Code:
Solicitation Number:
Small Business Information
13029 Danielson Street Suite 200 Poway, CA 92064-8811
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Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
Phase 1
Fiscal Year: 2002
Title: Hybrid Model Fusion for Gas Turbine Engine Diagnostics and Prognostics
Agency: NASA
Contract: NAS4-02038
Award Amount: $69,991.00


In Phase I IAC, teamed with Pratt & Whitney and Luppold & Associates, developed and demonstrated a hybrid modeling approach to enhance diagnostics and prognostics performance on F-117 engines. The hybrid model fuses a physics-based model developed by Pratt & Whitney called STORM with an empirical model that uses neural networks to monitor and quantify unmodeled and/or mismodeled engine phenomena that corrupt STORM?s diagnostic outputs. The resulting hybrid model is called the enhanced STORM or eSTORM. In Phase I eSTORM was demonstrated to work extremely well for limited conditions when processing simulated engine data. In Phase II IAC proposes to develop a standalone system for real time implementation of a full F-117 engine eSTORM. The system will cover the full C-17 flight envelope. The system will include interfaces to accept data from the C-17 aircraft bus and will be capable of on-wing operation. The system will be demonstrated using real F-117 inputs running in a test bench environment. Though developed explicitly for an F-117 application, the hybrid modeling concept developed on this Phase II is generic and can be used for eSTORM development on all Pratt & Whitney military and commercial engines.

Principal Investigator:

Tom Brotherton
Vice President, CTO

Business Contact:

Laura Goodrich
Vice President, Contracts
Small Business Information at Submission:

Intelligent Automation Corporation
13029 Danielson Street, Suite 200 Poway, CA 92064

EIN/Tax ID: 330876270
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No