Sequential Fuzzy Diagnosis for Condition Monitoring of Rolling Bearing Based on Neural Network

  • Huaqing Wang
  • Peng Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5264)


In the case of fault diagnosis of the plant machinery, diagnostic knowledge for distinguishing faults is ambiguous because definite relationships between symptoms and fault types cannot be easily identified. This paper propose a sequential fuzzy diagnosis method for condition monitoring of a rolling bearing used in a centrifugal blower by the possibility theory and a neural network. The possibility theory is used for solving the ambiguous problem of the fault diagnosis. The neural network is realized with a developed back propagation neural network. As input data for a neural network, the non-dimensional symptom parameters are also defined in time domain. Fault types of a rolling bearing can be effectively, sequentially distinguished on the basis of the possibilities of the normal state and abnormal states at early stage by the fuzzy diagnosis approach. Practical examples of diagnosis are shown in order to verify the efficiency of the method.


Sequential fuzzy diagnosis Neural network Possibility theory Condition monitoring Rolling bearing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pusey, H.C.: Machinery Condition Monitoring. Journal of Sound and Vibration 34(5), 6–7 (2000)Google Scholar
  2. 2.
    Mitoma, T., Wang, H., Chen, P.: Fault Diagnosis and Condition Surveillance for Plant Rotating Machinery Using Partially-linearized Neural Network. Computers & Industrial Engineering (2008), doi:10.1016/j.cie.2008.03.002HGoogle Scholar
  3. 3.
    Wang, H., Chen, P.: Fault Diagnosis for a Rolling Bearing Used in a Reciprocating Machine by Adaptive Filtering Technique and Fuzzy Neural Network. WSEAS Transactions on Systems 7, 1–6 (2008)Google Scholar
  4. 4.
    Williams, T., Ribadeneira, X., Billington, S., Kurfess, T.: Rolling Element Bearing Diagnostics in Run-to-failure Lifetime Testing. Mechanical Systems and Signal Processing 15, 979–993 (2001)CrossRefGoogle Scholar
  5. 5.
    Samanta, B., Al-Balushi, K.R., Al-Araimi, S.A.: Artificial Neural Networks and Genetic Algorithm for Bearing Fault Detection. Soft Computing 10, 264–271 (2006)CrossRefGoogle Scholar
  6. 6.
    Li, R.Q., Chen, J., Wu, X.: Fault Diagnosis of Rotating Machinery Using Knowledge-based Fuzzy Neural Network. Appl. Math. Mech-Eng. 27, 99–108 (2006)CrossRefGoogle Scholar
  7. 7.
    Blowerg, R.M.: Fault Diagnosis of Induction Machine Using Artificial Neural Network and Support Vector Machine. Dynamics of Continuous Discrete and Impulsive Systems-series A-Mathematical Analysis 13 (Part 2 Suppl. S) 658–661 (2006)Google Scholar
  8. 8.
    Saxena, A., Saad, A.: Evolving an Artificial Neural Network Classifier for Condition Monitoring of Rotating Mechanical Systems. Applied Soft Computing 7, 441–454 (2007)CrossRefGoogle Scholar
  9. 9.
    Bishop, M.C.: Neural Networks for Pattern Recognition. Oxford Univ. Press, Oxford (1995)Google Scholar
  10. 10.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, London (1972)Google Scholar
  11. 11.
    Bendat, J.S.: Probability Function for Random Processes: Prediction of Peak, Fatigue Damage, and Catastrophic Failure. NASA Report CR-33 (1969)Google Scholar
  12. 12.
    Chen, P., Taniguchi, M., Toyota, T., He, Z.: Fault Diagnosis Method for Machinery in Unsteady Operating Condition by Instantaneous Power Spectrum and Genetic Programming. Mechanical Systems and Signal Processing 19, 175–194 (2005)CrossRefGoogle Scholar
  13. 13.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton Univ. Press, Princeton (1976)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Huaqing Wang
    • 1
    • 2
  • Peng Chen
    • 1
  1. 1.Graduate School of BioresourcesMie UniversityMieJapan
  2. 2.School of Mech. & Elec. EngineeringBeijing University of Chemical TechnologyBeijingChina

Personalised recommendations