Fault Diagnoses Using Inverse Fuzzy Model

Hui Jun Zheng; Qiao Fu

doi:10.4028/www.scientific.net/AMM.571-572.171

Paper Titles

Identification and Optimization of Soil Water Movement Model Based on GA under Drip-Irrigation
p.148

Regression Analysis of Lower Extremity Injury Influencing Factors under Frontal Crash
p.152

Setting of Academic Warning Based on Multivariate Copula Functions
p.156

The Application of Iteration Learning Control in Silkworm Incubation Chamber System
p.164

Fault Diagnoses Using Inverse Fuzzy Model
p.171

A Community Detection Method Based on Multi-Objective Optimization Method
p.177

A High Precision Indoor Positioning System Based on VLC and Smart Handheld
p.183

A Hybrid IGA-SA Algorithm for Optimization Problems in Fault Diagnosis
p.187

Efficient Particle Swarm Optimization Algorithm Based on Affinity Propagation
p.191

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 571-572Fault Diagnoses Using Inverse Fuzzy Model

Fault Diagnoses Using Inverse Fuzzy Model

Abstract:

This paper investigates a new method of fault diagnoses and reliability analysis based on inverse fuzzy model method. The proposed method employs inverse fuzzy model solving method to estimate the component state based on the measured system performance and relationship about component state and system performance which is constructed by expert. The method is proved to be effective in fault diagnoses.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 571-572)

Pages:

171-176

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.571-572.171

Citation:

Cite this paper

Online since:

June 2014

Authors:

Hui Jun Zheng*, Qiao Fu

Keywords:

Fault Diagnosis, Inverse Fuzzy Model

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Jamshidi M, Vadiee N, Ross T, et al. Fuzzy logic and control software and hardware applications. PTRPrentice Hall, Englewood Cliffs, NJ, 1993 2.

DOI: 10.1002/acs.4480080310

Google Scholar

[2] Laviolette M, Seaman J, Barrett J, and Woodall W. A probabilistic and statistical view of fuzzy methods. IEEE Transaction on Fuzzy System. Vol 5, 1995: 37: 249-281.

DOI: 10.1080/00401706.1995.10484327

Google Scholar

[3] Feng-Tse Lin and Jing-Shing Yao, Applying Genetic Algorithms to Solve the Fuzzy Optimal Profit Problem. Journal Of Information Science And Engineering . Vol 8, 2002, 563-580.

Google Scholar

[4] H. Nomura, I. Hayashi, and N. Wakami, A Learning Method of Fuzzy Inference Rules by Descent Method, 1st IEEE Conf. on Fuzzy Systems, 203-210, (2011).

DOI: 10.1109/fuzzy.1992.258618

Google Scholar

[5] Zhou-Tian Fan and Qian-Sheng Cheng, A Survey on the Powers of Fuzzy Matrices and FBAMs, International Journal of Computational Cognition, Vol 2, Number 2, Pages 1–25, June (2004).

Google Scholar

[6] Martin Gavalec, Reachability of fuzzy matrix periods, Discrete Applied Mathematics. Vol 2, 2010, 61-79.

Google Scholar

[7] Martin Gavalec, Computing orbit period in Max-Min algebra, Discrete Applied Mathematics. Vol 5, 2010, 49-65.

DOI: 10.1016/s0166-218x(99)00174-2

Google Scholar

[8] J. R. Cavallaro and I. D. Walker. Failure Mode Analysis of a Proposed Manipulator-based Hazardous Material Retrieval System. In ANS 7th Topical Meeting on Robotics and Remote Systems, Vol 2, 1096-1102, Augusta, GA, (2010).

Google Scholar