Reciprocating Compressor Fault Classify Base on Multi-Component Singular Entropy

Yu Yuan; Ting Yu Wang; Bao Liang Li

doi:10.4028/www.scientific.net/AMR.505.221

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 505Reciprocating Compressor Fault Classify Base on...

Reciprocating Compressor Fault Classify Base on Multi-Component Singular Entropy

Abstract:

Condition monitoring of reciprocating machines through the analysis of their vibrations has been recognized to be a difficult issue, essentially because of the strong nonlinearity of the vibration signals. A new method of multi-component singular entropy is put forward to resolve this problem. Local Wave method is combined with Singular Entropy to extract features from the IMF of the vibration signals of reciprocating machines. And the features will be used as the input of ANFIS to classify and recognize the fault mode. The results are classified correctly. The conclusion shows that this method is feasible.

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Periodical:

Advanced Materials Research (Volume 505)

Pages:

221-226

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.505.221

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Online since:

April 2012

Authors:

Yu Yuan, Ting Yu Wang, Bao Liang Li

Keywords:

ANFIS, Fault Diagnosis, IMF, Local Wave, Singular Entropy

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References

[1] Xiaojiang Ma, Bo Yu，Zhixin Zhang. A new time domain analysis method—local wave method[J]. Journal of Vibration Engineering, 2000, 13(S): 219-224

Google Scholar

[2] Holger K, Thomas S. Nonlinear Time Series Analysis. Beijing; Tsinghua Press (2000)

Google Scholar

[3] Cao LY, Practical method for determining the minimum embedding dimension of a scalar time series, Physica D 1977,110:43-50

DOI: 10.1016/s0167-2789(97)00118-8

Google Scholar

[4] FRASER A M, SWINNEY H L, Independent coordinates for strange attractors from mutual information [J]Phys.Rev.A 1986,33:1134-40

DOI: 10.1103/physreva.33.1134

Google Scholar

[5] STANE K, MATJAZ P, Detecting chaos from a time series, Eur J.phys.2005,26:205-215

Google Scholar

[6] Zhangsui Xu，Liqing Fang，Xiwu wang. Fault information diagnose principle and application. [M]. Beijing: National Defence Industrial Press (2000)

Google Scholar

[7] Robert B, Henk V , Neuro-fuzzy methods for nonlinear system identification, Annual Reviews in control. 2003, 27: 73-85

Google Scholar