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http://dx.doi.org/10.5302/J.ICROS.2005.11.10.880

A Fault Detection and Isolation Method for Ammunition Transport Automation System  

Lee, Seung-Youn (충남대학교 전자공학과)
Kang, Kil-Sun (충남대학교 전자공학과)
Lyou, Joon (충남대학교 전자공학과)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.11, no.10, 2005 , pp. 880-887 More about this Journal
Abstract
This paper presents a fault diagnosis(detection and isolation) approach for the Ammunition Transport Automation system(ATAS). Due to limited time and information available during its cyclic operation, the on-line fault detection algorithm consists of sequential test logics referring to the normal states, which can be considered as a kind of expert system. If a failure were detected, the off-line isolation algorithm finds the fault location through trained ART2 neural network. By the results of simulations and some on-line field test, it has been shown that the presented approach is effective enough and applicable to related automation systems.
Keywords
ATAS; FDI; expert system; ART2 neural network; hybrid simulation;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 J. J. Gertler. 'Survey of model-based failure detection and isolation in complex plants,' IEEE Contr. Syst. Mag., vol. 8, pp. 3-11, 1988   DOI   ScienceOn
2 이인수, 신필재, 전기준, 'ART2 신경회로망을 이용한 선형 시스템의 다중고장진단,' 제어.자동화.시스템공학 논문지, 제3권, 제3호, pp. 244-251, 1997   과학기술학회마을
3 임호순, 정길도, '진동 신호 이용 모델 기반 모터 고장 검출 시스템 개발,' 제어.자동화.시스템공학 논문지, 제9권, 제11호, pp. 874-882, 2003   과학기술학회마을   DOI
4 R. Isermann, 'Fault diagnosis of machines via parameter estimation and knowledge processing,' Automatica, vol. 29, no. 4, pp. 815-835, 1993   DOI   ScienceOn
5 L. Ljung, System identification theory for the user, Prentice Hall, 1987
6 M. T. Hagan, Neural Network Design, PWS Publishing company, 1996
7 P. J. Tavner and J. Penman, Condition monitoring of electrical machines, Letchworth, UK: Research Studies Press, 1987
8 R. Isermann and B. Freyermuth, 'Process fault diagnosis based on process model knowledge-Part I: Principles for diagnosis with parameter estimation,' J. Dynamic Syst., Measurement, Contr., vol. 113, pp. 620-626, 1991   DOI
9 C. H. Pagli, Artificial Neural Networks for Intelligent Manufacturing, Chapman and Hall, 1994
10 J. Banks, et al., Discrete-Event System Simulaion, Prentice-Hall, pp. 12, 2001
11 M. A. Kramer and J. A. Lenard, 'Diagnosis using back propagation neural networks-analysis and criticism,' Computers Chem. Engng., vol. 14, no. 12, pp. 1323-1338, 1990   DOI   ScienceOn
12 R. Doraiswami and J. Jiang, 'Performance monitoring in expert control systems,' Automatica, vol. 25, no. 6, pp. 799-811, 1989   DOI   ScienceOn
13 R. Isermann, 'Model based fault detection and diagnosis methods,' Proc. Acc. pp. 1605-1609, 1995
14 B. Freyermuth, 'Knowledge based incipient fault diagnosis of industrial robots,' IFAC Proc. Fault Detection, Supervision and Safety for Technical Process, Baden-Baden, Germany, pp. 369-375, 1991
15 R. J. Patton, Fault diagnosis in dynamic systems, Prentice Hall, pp. 22-45, 1989