Neural Computing and Applications

, Volume 18, Issue 4, pp 397–405 | Cite as

Intelligent diagnosis method for a centrifugal pump using features of vibration signals

Original Article


In the field of machinery diagnosis, the utilization of vibration signals is effective in the detection of fault, because the signals carry dynamic information about the machine state. However, knowledge of a distinguishing fault is ambiguous because definite relationships between symptoms and fault types cannot be easily identified. This paper presents an intelligent diagnosis method for a centrifugal pump system using features of vibration signals at an early stage. The diagnosis algorithm is derived using wavelet transform, rough sets and a partially linearized neural network (PNN). ReverseBior wavelet function is used to extract fault features from measured vibration signals and to capture hidden fault information across optimum frequency regions. As the input parameters for the neural network, the non-dimensional symptom parameters that can reflect the characteristics of a signal are defined in the amplitude domain. The diagnosis knowledge for the training of the PNN can be acquired by using the rough sets. We also propose a diagnosis method based on the PNN, one which can deal with the ambiguity problem of condition diagnosis, and distinguish fault types on the basis of the possibility distributions of symptom parameters automatically. The decision method of optimum frequency region for extracting feature signals is also discussed using real plant data. Practical examples of diagnosis for a centrifugal pump system are shown in order to verify the efficiency of the method.


Intelligent diagnosis Neural network Rough sets Wavelet transform Vibration signal Centrifugal pump 


  1. 1.
    Carsten Sk, Vincent C, Roozbeh IZ (2006) Model based fault detection in a centrifugal pump application. IEEE Trans Cont Syst Tech 14(2):204–215CrossRefGoogle Scholar
  2. 2.
    Rajakarunakarana S, Venkumara P, Devaraja D, Surya Prakasa Raob K (2008) Artificial neural network approach for fault detection in rotary system. Appl Soft Comput 8(1):740–748CrossRefGoogle Scholar
  3. 3.
    Perovic S, Unsworth PJ, Higham EH (2001) Fuzzy logic system to detect pump faults from motor current spectra, IEEE, Thirty-Sixth IAS Annual Meeting.Google Scholar
  4. 4.
    Jing Lin, Liangsheng Qu (2000) Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis. J Sound Vibrat 234(1):135–148. doi:10.1006/jsvi.2000.2864 CrossRefGoogle Scholar
  5. 5.
    Liu B, Ling S-F (1999) On the selection of informative wavelets for machinery diagnosis. Mech Syst Signal Process 13(1):145–162. doi:10.1006/mssp. 1998.0177 CrossRefGoogle Scholar
  6. 6.
    Matuyama H (1991) Diagnosis Algorithm. J JSPE 75(3):35–37Google Scholar
  7. 7.
    Zhu QB (2006) Gear fault diagnosis system based on wavelet neural networks. Dynamics of Continuous Discrete and Impulsive Systems-series A-Mathematical Analysis, vol 13, Part 2 Suppl S, pp 671–673Google Scholar
  8. 8.
    Becerikli Y (2004) On three intelligent systems: dynamic neural, fuzzy and wavelet networks for training trajectory. Neural Comput Appl 13(4):339–351. doi:10.1007/s00521-004-0429-9 CrossRefGoogle Scholar
  9. 9.
    Fang RM (2006) Fault diagnosis of induction machine using artificial neural network and support vector machine. Dynamics of Continuous Discrete and Impulsive Systems-series A-Mathematical Analysis, vol 13, Part 2 Suppl S, pp 658–661Google Scholar
  10. 10.
    Saxena A, Saad A (2007) Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. Appl Soft Comput 7(1):441–454. doi:10.1016/j.asoc.2005.10.001 CrossRefGoogle Scholar
  11. 11.
    Samanta B, Al-Balushi KR (2003) Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech Syst Signal Process 17(2):317–328. doi:10.1006/mssp. 2001.1462 CrossRefGoogle Scholar
  12. 12.
    Li RQ, Chen J, Wu X (2006) Fault diagnosis of rotating machinery using knowledge-based fuzzy neural network. Appl Math Mech Eng 27(1):99–108MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Christopher BMI (1995) Neural networks for pattern recognition. Oxford University Press, NYGoogle Scholar
  14. 14.
    Cudina M (2003) Detection of cavitation phenomenon centrifugal pump using audible sound. Mech Syst Signal Process 17(6):1335–1347. doi:10.1006/mssp. 2002.1514 CrossRefGoogle Scholar
  15. 15.
    Daubechie I (1990) The wavelet transform, time–frequency localization and signal analysis. IEEE Trans Inf Theory 36:961–1005. doi:10.1109/18.57199 CrossRefGoogle Scholar
  16. 16.
    Prabhakar S, Mohanty AR, Sekhar AS (2002) Application of discrete wavelet transform for detection of ball bearing race faults. Tribol Int 35:793–800. doi:10.1016/S0301-679X(02)00063-4 CrossRefGoogle Scholar
  17. 17.
    Mallat SG (1989) A theory for Multi-resolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693. doi:10.1109/34.192463 MATHCrossRefGoogle Scholar
  18. 18.
    Fukunaga K (1972) Introduction to Statistical Pattern Recognition. Academic Press, LondonGoogle Scholar
  19. 19.
    Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:344–356MathSciNetCrossRefGoogle Scholar
  20. 20.
    Milton RS, Uma Maheswari V, Siromoney Arul (2004) Rough sets and relational learning. Lect Notes Comput Sci 3100:321–337Google Scholar

Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  1. 1.Graduate School of BioresourcesMie UniversityTsuJapan
  2. 2.School of Mechanical and Electrical EngineeringBeijing University of Chemical TechnologyBeijingChina

Personalised recommendations