Multivariable dynamic system fault diagnosis using nonlinear spectrum and SVM fusion

2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) ◽

10.1109/civemsa.2015.7158593 ◽

2015 ◽

Author(s):

Jialiang Zhang ◽

Jianfu Cao ◽

Feng Gao

Keyword(s):

Fault Diagnosis ◽

Dynamic System ◽

Nonlinear Spectrum

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Fault diagnosis of multivariable dynamic system based on nonlinear spectrum and support vector machine

2015 34th Chinese Control Conference (CCC) ◽

10.1109/chicc.2015.7260605 ◽

2015 ◽

Author(s):

Zhang Jialiang ◽

Cao Jianfu

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Dynamic System ◽

Support Vector ◽

Nonlinear Spectrum

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Feature extraction of nonlinear spectrum and fault diagnosis for multivariable dynamic system

2013 25th Chinese Control and Decision Conference (CCDC) ◽

10.1109/ccdc.2013.6561779 ◽

2013 ◽

Author(s):

Jianfu Cao ◽

Jialiang Zhang ◽

Jiguang Zheng

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Dynamic System ◽

Nonlinear Spectrum

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Fault Diagnosis of Dynamic System by Means of parameter estimation

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)54885-5 ◽

1988 ◽

Vol 21 (9) ◽

pp. 1159-1164

Author(s):

Li Zhi ◽

Zheng Yong

Keyword(s):

Parameter Estimation ◽

Fault Diagnosis ◽

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Neural fault diagnosis and fuzzy fault control for a complex linear dynamic system

Proceedings of 1995 IEEE International Conference on Fuzzy Systems. The International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium ◽

10.1109/fuzzy.1995.409803 ◽

2002 ◽

Author(s):

Chingj-Yu Tyan ◽

P.P. Wang ◽

D.R. Bahler

Keyword(s):

Fault Diagnosis ◽

Dynamic System ◽

Linear Dynamic System ◽

Linear Dynamic ◽

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Fault Diagnosis in a Large Dynamic System: Experiments on a Training Simulator

IEEE Transactions on Systems Man and Cybernetics ◽

10.1109/tsmc.1986.289289 ◽

1986 ◽

Vol 16 (1) ◽

pp. 129-141 ◽

Author(s):

Yuan-liang Su ◽

T. Govindaraj

Keyword(s):

Fault Diagnosis ◽

Dynamic System ◽

Training Simulator

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Research on the non-liner dynamic system theory into teciprocating compressor fault diagnosis power

The 2002 International Conference on Control and Automation, 2002. ICCA. Final Program and Book of Abstracts. ◽

10.1109/icca.2002.1229774 ◽

2005 ◽

Author(s):

Minlin Liu ◽

Boyun Liu ◽

Hanbao Chang

Keyword(s):

System Theory ◽

Fault Diagnosis ◽

Dynamic System ◽

Dynamic System Theory

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Study of the non-liner dynamic system theory for reciprocating compressor fault diagnosis

2010 International Conference on Computer Application and System Modeling (ICCASM 2010) ◽

10.1109/iccasm.2010.5623041 ◽

2010 ◽

Author(s):

Ruilin Lin ◽

Boyun Liu ◽

Qi Liu

Keyword(s):

System Theory ◽

Fault Diagnosis ◽

Dynamic System ◽

Reciprocating Compressor ◽

Dynamic System Theory

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Bifurcation and Chaos Research on Dry Friction System in the Spring Fault Condition

International Journal of Modern Physics B ◽

10.1142/s0217979203022519 ◽

2003 ◽

Vol 17 (22n24) ◽

pp. 4395-4401

Author(s):

Wenli Zhao ◽

Xiaojun Zhou ◽

Guanwei Luo

Keyword(s):

Fault Diagnosis ◽

Theoretical Analysis ◽

Dynamic System ◽

Periodic Solutions ◽

Numerical Computation ◽

Dynamic Behavior ◽

Dry Friction ◽

Two Dimensional ◽

Bifurcation And Chaos

In this paper, the periodic solutions and stability in the dry friction dynamic system are researched by two-dimensional Poincaremap when the fault has happened in the system because of the spring failure. As a result of numerical computation and theoretical analysis, the bifurcation and chaos will arise when the parameters of system are changed. It is very important to analyze the dynamic behavior to conduct fault diagnosis.

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Dynamic system identification and model-based fault diagnosis of an industrial gas turbine prototype

Mechatronics ◽

10.1016/j.mechatronics.2006.01.002 ◽

2006 ◽

Vol 16 (6) ◽

pp. 341-363 ◽

Author(s):

Silvio Simani ◽

Cesare Fantuzzi

Keyword(s):

Fault Diagnosis ◽

System Identification ◽

Dynamic System ◽

Gas Turbine ◽

Model Based ◽

Industrial Gas Turbine

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The application of a correlation dimension in large rotating machinery fault diagnosis

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1243/0954406001523155 ◽

2000 ◽

Vol 214 (7) ◽

pp. 921-930 ◽

Author(s):

W Wang ◽

J Chen ◽

Z Wu

Keyword(s):

Fault Diagnosis ◽

Dynamic System ◽

Influencing Factors ◽

Correlation Dimension ◽

Rotating Machinery ◽

Vibration Signals ◽

Machinery Fault Diagnosis

This paper reports on the application of the correlation dimension in large rotating machinery fault diagnosis. The Grassberger-Procaccia algorithm and its modified version are introduced. Some important influencing factors relating directly to the computational precision of the correlation dimension are discussed. Industrial vibration signals measured from large rotating machinery with different faults are researched using the above-mentioned methods. The results show that the correlation dimension can provide some intrinsic information on an underlying dynamic system and can be used to classify different faults intelligently.

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