State Prognostics Based Health Management for Complex Systems

Author

Aofu Zhang ; Lirong Cui ; Pu Zhang

Author_Institution

Sch. of Manage. & Econ., Beijing Inst. of Technol., Beijing, China

fYear

2013

fDate

16-17 Nov. 2013

Firstpage

458

Lastpage

461

Abstract

The research on Prognostics and Health Management (PHM) for complex systems is a hot topic in the current reliability field, its main idea is to change the system logistics from state monitoring to state management, which makes people pay an attention actively on the system failures and risk of performance, and do the health management for complex systems. Thus, the research is significant in both theory and practice. The contents of the research include three areas in PHM. Optimal allocation and reliability evaluation for sensor systems, which is a base for obtaining the system operation information, Modeling and method for prognostics under uncertainty conditions, which is a basic work and content for PHM, Health management and optimal decisions is the heart of PHM. The research will build an essential base on applications of PHM for complex systems in the future.

Keywords

condition monitoring; failure analysis; large-scale systems; reliability theory; sensors; PHM; complex systems; optimal allocation; optimal decisions; performance risk; prognostics method; reliability evaluation; sensor systems; state management; state monitoring; state prognostics-based health management; system failures; system logistics; system operation information; uncertainty conditions; Degradation; Hidden Markov models; Maintenance engineering; Predictive models; Prognostics and health management; Reliability; Sensor systems; Health management; Management strategies; Models and methods; Sensor system; State prognostics;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology and Applications (ITA), 2013 International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4799-2876-7

Type

conf

DOI

10.1109/ITA.2013.111

Filename

6710027