C05-31 Decision Support for PHM - Meeting Prognostic Requirements
Objective:
The objective of this project is to continue and complete the multiple LRU decision analysis, and extend and adapt the PHM (Prognostic Health Management) modeling methodology developed in C04-18 to specifically address system prognostic requirements by considering:
- how to assess if prognostic requirements are practical to meet
- how to determine if a proposed system design actually meets the prognostic requirements (and how to build a business case to show it)
- how to determine the cost of meeting those prognostic requirements
- how can the prognostic requirements can be passed to subcontractors (specification)
- what level of the system should the prognostic requirements be applied at.
Background:
The following requirement appeared in a recent United States Department of Defense (DoD) statement of work:
"The [item] shall incorporate an embedded prognostic capability that will enhance availability and reduce support costs by predicting mission critical failures. Prognostics shall predict at least 70% (with a 95% goal) of the mission critical failures from 480 hours to 96 hours in advance of occurrence with 80% probability."
Demonstrating that a proposed design approach can meet this prognostic requirement and assessing whether the combination of PHM methodologies you are considering can meet this requirement are difficult (if not impossible) tasks today - let alone proving to your customer that you have actually met this requirement.
The essence of prognostics is the estimation of remaining life in terms that are useful to the maintenance decision process. All PHM approaches are essentially the extrapolation of trends based on recent observations to estimate remaining life. Unfortunately, this calculation alone does not provide sufficient information to form a decision or to determine corrective action. Without accommodating the corresponding measures of the uncertainty associated with the calculation, remaining life projections have little practical value, [1]. It is the accommodation of the corresponding uncertainties (decision making under uncertainty) that is at the heart of understanding and being able to both effectively perform PHM and to be able to provide a business case that legitimately addresses the requirement above.[1] S. Engel, B. Gilmartin, K. Bongort, and A. Hess, "Prognostics, the Real Issues Involved with Predicting Life Remaining," Proceedings of the IEEE Aerospace Conference, pp. 457-469, 2000.
Approach:
This project will adapt and extend the PHM modeling methodology developed in the CALCE Project C04-18. The modeling methodology developed in C04-18 has demonstrated a stochastic approach that quantitatively measures the cost, availability, and failures avoided impact of various PHM approaches (LCM - life consumption monitoring and HM - health monitoring). For application to real systems, this methodology must be extended to handle PHM solutions that are combinations of LCM, HM, and scheduled maintenance (including "Canary" type approaches, [2]). The C04-18 methodology must also be combined with actual prognostics analysis approaches that have been developed for condition-based maintenance (e.g., [3]) in order to address the prognostics requirements problem articulated above.
As a byproduct of the proposed work, enabling addressing requirements like the one above would considerably advance the ability to manage and specify concepts such as Failure Free Operating Period (FFOP) and Maintenance Free Operating Period (MFOP).[2] D. Palmer and M. Kendig, "Prognostics in Military-based Digital Electronic Assemblies", Interagency Integrated Vehicle Health Management Diagnostic," Prognostic Technical Interchange Meeting, Huntsville, AL, October 30, 2003.
[3] M. J. Roemer, G. J., Kacprazynski, E. O. Nwadiogbu, and G. Bloor, "Development of Diagnostic and Prognostic Technologies for Aerospace Health Management Applications," Proceedings of the IEEE Aerospace Conference, pp. 3139-3147, 2001.Reports:
Final Project Report