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Research Papers

Genetic Integration of Different Diagnosis Methods and/or Fault Features for Improvement of Diagnosis Accuracy

[+] Author and Article Information
Dou Wei1

School of Energy Science and Engineering, Harbin Institute of Technology, Box 458, No. 92, West Da-Zhi Street, Harbin, Heilongjiang 150001, Chinadwdqpi@126.com

Liu Zhan-Sheng

School of Energy Science and Engineering, Harbin Institute of Technology, Box 458, No. 92, West Da-Zhi Street, Harbin, Heilongjiang 150001, China

1

Corresponding author.

J. Vib. Acoust 131(1), 011002 (Dec 29, 2008) (8 pages) doi:10.1115/1.2980379 History: Received August 06, 2007; Revised May 11, 2008; Published December 29, 2008

Genetic integration of different diagnosis methods and/or fault features is proposed in this paper for improvement of diagnosis accuracy, and a weighted matrix is established by integrating neural network and artificial immune diagnoses, wavelet packet energy, and bispectrum features using genetic algorithm for the diagnosis of a rotating machinery to prove the validity of this approach. Experimental results indicate that both diagnosis accuracy and robustness of diagnosis system can be improved by integrating different diagnosis methods and/or fault features. It is therefore concluded that integration of different diagnosis methods and/or fault features is one of the ways to achieve more accurate diagnosis of machinery.

Copyright © 2009 by American Society of Mechanical Engineers
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References

Figures

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Recognition of antigen and antibody

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Block diagram of combination diagnosis

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Buildup of rotor-bearing system test-bed for a 600 MW supercritical steam turbine

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Rotor-bearing system for test-bed of a 600 MW supercritical steam turbine

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Time wave of normal rotor vibration

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Figure 1

Block diagram of serial integration

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Block diagram of parallel integration

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String representation of weight matrix

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Two-point crossover operator

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Time wave of fault due to rotor imbalance

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Time wave of vibration due to rotor misalignment

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Time wave of steam exciting vibration

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Time wave of vibration due to loose bearing pedestal

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Fault diagnosis process chart

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Figure 15

Diagnosis accuracies by integration of different features and/or different diagnosis methods

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