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Reseach Article

AI Technique in Diagnostics and Prognostics

Published on February 2013 by A. Poongodai, S. Bhuvaneswari
National Conference on Future Computing 2013
Foundation of Computer Science USA
NCFC - Number 1
February 2013
Authors: A. Poongodai, S. Bhuvaneswari
1d8e41d4-671a-4877-997e-f1d52b7095ef

A. Poongodai, S. Bhuvaneswari . AI Technique in Diagnostics and Prognostics. National Conference on Future Computing 2013. NCFC, 1 (February 2013), 1-4.

@article{
author = { A. Poongodai, S. Bhuvaneswari },
title = { AI Technique in Diagnostics and Prognostics },
journal = { National Conference on Future Computing 2013 },
issue_date = { February 2013 },
volume = { NCFC },
number = { 1 },
month = { February },
year = { 2013 },
issn = 0975-8887,
pages = { 1-4 },
numpages = 4,
url = { /proceedings/ncfc/number1/10400-1001/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Future Computing 2013
%A A. Poongodai
%A S. Bhuvaneswari
%T AI Technique in Diagnostics and Prognostics
%J National Conference on Future Computing 2013
%@ 0975-8887
%V NCFC
%N 1
%P 1-4
%D 2013
%I International Journal of Computer Applications
Abstract

Artificial Intelligence is the science and engineering of making intelligent machine especially intelligent computer programs. Activities in AI include Searching, Recognizing patterns and Making logical inferences. This paper discuss on Artificial intelligence technique used for System diagnostics and prognostics. Three approaches of AI for diagnostics and prognostics are 1. Rule based diagnostics 2. Model based diagnostics and 3. Data Driven Approaches. System diagnosis is the process of inferring the cause of any abnormal or unexpected behavior. A prognostic is predicting the time at which a system or a component will no longer perform its intended function.

References
  1. Ann Patterson-Hine, Gordon Aaseng, Gautam Biswas, Sriram Narasimhan, Krishna Pattipati, A Review of Diagnostic Techniques for ISHM Applications
  2. Mark Schwabacher and Kai Goebel, NASA Ames Research Center, A Survey of Artificial Intelligence for Prognostics
  3. C. Angeli, A. Chatzinikolaou, On-Line Fault Detection Techniques for Technical Systems: A Survey,International Journal of Computer Science & Applications © 2004, Vol. I, No. 1, pp. 12 – 30
  4. Beshears, R. and Butler, L. 2005. Designing For Health; A Methodology For Integrated Diagnostics / Prognostics. Proceedings of IEEE Autotestcon. New York: IEEE
  5. Thair Nu, Phyu, Survey of Classification technique in data mining. Proc International Multi Conference on Engineers and Computer Society, Volume 1, 2009
  6. Beshears, R. and Butler L,. 2005. Designing For Health; A Methodology For Integrated Diagnostics/Prognostics Proceedings of IEEE Autotestcon. New York: IEEE.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Intelligence Diagnostics Prognostics Rule Based System Model Based Ai Approach Data Driven Approach