N101-006 TITLE: Prognostic & Health Management (PHM) Technologies for Unmanned Aerial Vehicles (UAV)

TECHNOLOGY AREAS: Sensors

ACQUISITION PROGRAM: PMA-290, Maritime Patrol and Reconnaissance Aircraft

RESTRICTION ON PERFORMANCE BY FOREIGN CITIZENS (i.e., those holding non-U.S. Passports): This topic is "ITAR Restricted." The information and materials provided pursuant to or resulting from this topic are restricted under the International Traffic in Arms Regulations (ITAR), 22 CFR Parts 120 - 130, which control the export of defense-related material and services, including the export of sensitive technical data. Foreign Citizens may perform work under an award resulting from this topic only if they hold the "Permanent Resident Card", or are designated as "Protected Individuals" as defined by 8 U.S.C. 1324b(a)(3). If a proposal for this topic contains participation by a foreign citizen who is not in one of the above two categories, the proposal will be rejected.

OBJECTIVE: Develop a diagnostic and prognostic architecture to enable a condition based maintenance system for Unmanned Aerial Vehicles (UAV).

DESCRIPTION: With the advent of significantly more complex Unmanned Air Vehicles (UAV) in the DoD inventory and the unique challenges and opportunities they present, integration of the typical Prognostics & Health Management (PHM) System from a manned platform is not always optimal. UAVs present unique challenges in the diagnostics, prognostics and health monitoring of engines and drive systems, sensors (electro-optical/infrared (EO/IR), Radar, etc), electro-mechanical actuator (EHA), and communications, during endurance missions. Wherein the pilot debrief is often used to identify changes in engine performance or aircraft handling characteristics for maintenance purposes, without a pilot in the loop, the PHM system on UAVs must be relied upon to a greater extent to report propulsion faults and drive maintenance actions. Propulsion faults such as compressor surge/stall, vibrations, and screech can often be identified by auditory or vibration changes in a manned platform. This detection is obviously not available on unmanned platforms, making it more difficult to detect, diagnose, and repair propulsion system problems.

While there are many challenges associated with UAVs, there are also many unique opportunities presented with integrating a PHM system. With the UAV under continual downlink connectivity, there exists the possibility to perform highly complex diagnostic routines and prognostic algorithms near real time (NRT) offboard in the ground control station (GCS). This can free up limited computational resources and memory capacity for more safety critical routines. UAVs are also highly electronic-digital systems that already utilize a vast array of system sensors embedded with various flight critical and mission essential components for system control. As such, these sensors could be easily integrated and networked with the PHM system and potentially provide redundant fault tolerant adaptive control.

Based on these unique characteristics, explore innovative PHM concepts that optimize diagnostic, prognostic and health monitoring functionality for UAVs. Design approaches should assure full coverage of all safety critical, flight critical and mission essential hardware while minimizing onboard space and weight. The system should identify an optimal design approach and architecture to effectively and efficiently utilize both onboard and offboard processing capability. Approaches should fully support the implementation of condition based maintenance (CBM) practices and enable simplified Organic to Depot (O-D) level maintenance in an autonomic logistics environment. The PHM system must also be capable of automatically adjusting for lack/loss of datalink bandwidth such that no data is lost and no safety or flight critical faults are missed. With an emphasis placed on endurance and maximizing fuel capacity, the UAV PHM system will need to adhere to stringent weight and space constraints.

Coordination with a UAV manufacturer is recommended.

PHASE I: Define and determine the feasibility of providing a dependable and robust PHM system for enabling condition based maintenance on UAVs.

PHASE II: Provide a model of the recommended architecture, hardware, algorithms and demonstrate the ability to detect faults and drive CBM actions. Develop, demonstrate and validate the final application for the model maximizing PHM functionality while meeting stated requirements. Demonstrate the capability of the prototype equipment.

PHASE III: Integrate the system on-board an aircraft with flight qualified hardware and software. Incorporate the technology with a defense program of record and determine the system�s compatibility with legacy and future applications. Transition the completed UAV PHM system to appropriate platforms.

PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: Unmanned Aerial Vehicles are increasingly being used for border patrol, land and atmospheric surveys, and law enforcement. These UAVs would benefit from a PHM/CBM system by minimizing repair cost and increasing time-on-station. A robust PHM system would also provide increase safety in a community environment that may assist with Federal Aviation Administration (FAA) commercial airspace integration.

REFERENCES:
1. Henley, Simon; Currer, Ross; Sheuren, Bill; Hess, Andy; and Goodman, Geoffrey. "Autonomic Logistics�The Support Concept for the 21st Century." IEEE Proceedings, Track 11, paper zf11_0701. http://ieeexplore.ieee.org/search/srchabstract.jsp?arnumber=877915&isnumber=18960&punumber=7042&k2dockey=877915@ieeecnfs&query=%28%28autonomic+logistics%29%3Cin%3Emetadata%29&pos=1&access=no

2. Byer, Bob; Hess, Andy; and Fila, Leo. "Writing a Convincing Cost Benefit Analysis to Substantiate Autonomic Logistics." Aerospace Conference 2001, IEEE Proceedings, Vol. 6, pp. 3095-3103; http://ieeexplore.ieee.org/search/srchabstract.jsp?arnumber=877915&isnumber=18960&punumber=7042&k2dockey=877915@ieeecnfs&query=%28%28autonomic+logistics%29%3Cin%3Emetadata%29&pos=1&access=no

3. SAE E-32 Committee Documents. http://www.sae.org/servlets/works/documentHome.do?comtID=TEAE32

4. IEEE Aerospace Conference Proceedings for 2001 and 2002, Track 11 PHM.

KEYWORDS: unmanned aerial vehicle; diagnostic; prognostic; sensor; prognositcs health management; condition based maintenance

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