|Acquisition Program: ||JSF|
| ||RESTRICTION ON PERFORMANCE BY FOREIGN NATIONALS: 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 nationals 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 national who is not in one of the above two categories, the proposal may be rejected.|| Objective: ||Develop and demonstrate new and advanced prognostic models for communication, navigation and identification (CNI) systems and their components.
|| Description: ||In order to fully enable the predictive part of any PHM concept, there must be some capability to relate detected incipient fault conditions to accurate remaining useful life predictions for any point in time. Key to accomplishing this is being able to understand incipient fault-to-failure progression characteristics for the component and/or subsystem of interest and having verifiable prognostic models. This may be accomplished through the merging of an understanding of the component’s physics of failure, analytical models, physical models, statistical techniques, and actual failure data. It is desirable that the models be supported and driven by existing parameters and measurands.
This effort will develop, demonstrate, and apply these advanced prognostic and remaining useful life models in support of the predictive part of PHM on aircraft CNI systems and their subsystem components. Subsystems include aircraft global positioning systems (GPS), inertial navigation systems (INS) including landing aids, identification friend or foe (IFF) systems, radar altimeter, and voice and data communications systems. With the criticality of radar systems to complete
aircraft missions, it is important that the user be able to accurately diagnose faults and predict failures and remaining life of these components. Because of the large amount and variety of components and devices used in CNI systems, new and innovative approaches, models, and methodologies will be required.
|| ||PHASE I: Define the techniques and processes needed to relate remaining useful life (RUL) predictions to detectable conditions in one or more aircraft CNI subsystems and their components. Determine the feasibility of developing advanced prognostic models, statistical techniques and other programs required for a specific CNI subsystem application and/or its components. Determine the required inputs to the models; outline a method of extracting them from an installed CNI subsystem and/or specific component; and define required user interfaces.
|| ||PHASE II: Assess the application boundaries, accuracy, and limitations for these modeling techniques. Demonstrate the prototype prognostics models, techniques and supporting programs for a specific CNI subsystem and its components.
|| ||PHASE III: Finalize these prognostics models with specific JSF CNI system applications. Develop, validate, and deliver a complete set of application modeling programs and techniques to be used on JSF CNI subsystems, circuits and components. Provide software programs, tools, and procedures for integrating these capabilities within the JSF PHM system and transition some or all of these modeling programs and capabilities into the F-35 JSF program.
|| ||PRIVATE SECTOR COMMERCIAL POTENTIAL: CNI systems have applications in both commercial and military applications. These advanced models would be applicable in the private sector to many CNI applications that will be applying diagnostic, prognostic, and health management capabilities. Any results and understanding gained from applying these failure progression rate models to particular CNI subsystems will provide a significant crossover benefit to other similar applications, commercial or military.
|| 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.
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.
3. IEEE Aerospace Conference Proceedings for 2001 and 2002 Track 11 PHM.|
|Keywords: ||Diagnostics; Prognostics; Modeling; Remaining Useful Life; Remaining Predictions; Failure Prediction|