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Fault Diagnosis Method of Machinery Based on Fisher’s Linear Discriminant and Possibility Theory

  • Weijuan Jiang
  • Zhongxing Li
  • Ke Li
  • Hongtao Xue
  • Peng Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7390)

Abstract

This paper proposes a new condition diagnosis method for plant machinery using Fisher’s linear discriminant and possibility theory. The non-dimensional symptom parameters (NSPs) are defined to reflect the features of the vibration signals measured in each state. Fisher’s linear discriminant is used to project the multiple SPs from a high dimensional space to a low dimensional space for distinguishing states, and discriminant rules are set by possibility theory. Moreover, sequential diagnosis is also proposed by which the conditions of the machinery can be identified sequentially. Sensitive evaluation method for selecting good symptom parameters using Distinction Index (DI) is also suggested for detecting faults in rotating machinery. Finally, practical examples of the diagnosis for rotating machine are shown to verify the efficiency of the method.

Keywords

Fault diagnosis Rotating machinery Fisher’s linear discriminant Possibility theory Sequential method 

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References

  1. 1.
    Saxena, A., Saad, A.: Evolving An Artificial Neural Network Classifier for Condition Monitoring of Rotating Mechanical Systems. Applied Soft Computing 7(1), 441–454 (2007)CrossRefGoogle Scholar
  2. 2.
    Lei, Y., He, Z., Zi, Y.: Application of An Intelligent Classification Method to Mechanical Fault Diagnosis. Expert Systems with Applications 36, 9941–9948 (2009)CrossRefGoogle Scholar
  3. 3.
    Dong, X.M., Han, J., Shen, H.W.: Full Information Fusion and Its Application in Fault Diagnosis for Rotary Machinery. Applied Mechanics and Materials 66-68, 1315–1319 (2011)CrossRefGoogle Scholar
  4. 4.
    Wang, H.Q., Chen, P.: Intelligent Diagnosis Method for A Centrifugal Pump using Features of Vibration Signals. Neural Computing & Applications 18(4), 397–405 (2009)CrossRefGoogle Scholar
  5. 5.
    Wang, H.Q., Chen, P.: Sequential Diagnosis for Rolling Bearing using Fuzzy Neural Network. In: Proceedings of the 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 56–61. IEEE Press, Xian (2008)CrossRefGoogle Scholar
  6. 6.
    Mitoma, T., Wang, H.Q., Chen, P.: Fault Diagnosis and Condition Surveillance for Plant Rotating Machinery using Partially-linearized Neural Network. Computers & Industrial Engineering 55, 783–794 (2008)CrossRefGoogle Scholar
  7. 7.
    Li, C., Liang, M.: Time-frequency Signal Analysis for Gearbox Fault Diagnosis using A Generalized Synchrosqueezing Transform. Mechanical Systems and Signal Processing 26, 205–217 (2012)CrossRefGoogle Scholar
  8. 8.
    Chiang, L.H., Kotanchek, M.E., Kordon, A.K.: Fault diagnosis based on Fisher discriminant analysis and support vector machines. Computer and Chemical Engineering 28, 1389–1401 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Weijuan Jiang
    • 1
    • 2
  • Zhongxing Li
    • 1
  • Ke Li
    • 2
  • Hongtao Xue
    • 2
  • Peng Chen
    • 2
  1. 1.School of Automotive and Traffic EngineeringJiangsu UniversityZhenjiangChina
  2. 2.Graduate School of BioresourcesMie UniversityTsuJapan

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