Lifecycle Prognostics Architecture for Selected High-Cost Active Components

Lybeck, N. ; Pham, B. ; Tawfik, M. ; Coble, J. B. ; Meyer, R. M. ; Ramuhalli, P. ; Bond, L. J.

Idaho National Laboratory

关键词: Probability;    Aging;    Prognostics;    Monitoring;    Management;   

DOI  :  10.2172/1027927
RP-ID  :  INL/EXT-11-22915
RP-ID  :  DE-AC07-05ID14517
RP-ID  :  1027927

美国|英语

来源: UNT Digital Library

【 摘 要 】

There are an extensive body of knowledge and some commercial products available for calculating prognostics, remaining useful life, and damage index parameters. The application of these technologies within the nuclear power community is still in its infancy. Online monitoring and condition-based maintenance is seeing increasing acceptance and deployment, and these activities provide the technological bases for expanding to add predictive/prognostics capabilities. In looking to deploy prognostics there are three key aspects of systems that are presented and discussed: (1) component/system/structure selection, (2) prognostic algorithms, and (3) prognostics architectures. Criteria are presented for component selection: feasibility, failure probability, consequences of failure, and benefits of the prognostics and health management (PHM) system. The basis and methods commonly used for prognostics algorithms are reviewed and summarized. Criteria for evaluating PHM architectures are presented: open, modular architecture; platform independence; graphical user interface for system development and/or results viewing; web enabled tools; scalability; and standards compatibility. Thirteen software products were identified and discussed in the context of being potentially useful for deployment in a PHM program applied to systems in a nuclear power plant (NPP). These products were evaluated by using information available from company websites, product brochures, fact sheets, scholarly publications, and direct communication with vendors. The thirteen products were classified into four groups of software: (1) research tools, (2) PHM system development tools, (3) deployable architectures, and (4) peripheral tools. Eight software tools fell into the deployable architectures category. Of those eight, only two employ all six modules of a full PHM system. Five systems did not offer prognostic estimates, and one system employed the full health monitoring suite but lacked operations and maintenance support. Each product is briefly described in Appendix A. Selection of the most appropriate software package for a particular application will depend on the chosen component, system, or structure. Ongoing research will determine the most appropriate choices for a successful demonstration of PHM systems in aging NPPs.