Multi Fault Diagnosis of the Centrifugal Pump Using the Wavelet Transform and Principal Component Analysis

  • Berli Kamiel
  • Kris McKee
  • Rodney Entwistle
  • Ilyas Mazhar
  • Ian Howard
Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 21)


In this paper, the features of vibration signals from normal and faulty conditions of a centrifugal pump were extracted from time-domain data using the discrete wavelet transform (DWT). The DWT with Multi Resolution Analysis (MRA) was used to pre-process raw vibration signals prior to extraction of statistical features. The features obtained were used as input to Principal Component Analysis (PCA). A method based on PCA was then developed to build a framework for multi-fault diagnosis of centrifugal pumps by using historical normal conditions. The fault detection was determined using T2-statistics and Q-statistics while fault identification was carried out through the combination of loadings and scores of principal components (PCs). The normal and faulty conditions of the centrifugal pump were collected from the Spectra Quest Machinery Fault Simulator. Various fault conditions were investigated in the experiment including cavitation, impeller fault, and combination of impeller fault and cavitation. The results showed that combined wavelet-PCA can be used to detect multi-faults in the centrifugal pump. Furthermore, the combination of loadings and scores of PCs was demonstrated which showed effective fault identification.


  1. 1.
    Lattin J, Carroll JD, Green PE (2003) Analyzing multivariate data. Thomson Learning Inc., New york, USAGoogle Scholar
  2. 2.
    Jolliffe IT (2002) Principal component analysis. Springer, Secaucus, USAMATHGoogle Scholar
  3. 3.
    Ahmed M, Baqqar M, Gu F, Ball AD (2012) Fault detection and diagnosis using principal component analysis of vibration data from a reciprocating compressor. In: International conference on control (CONTROL), 2012 UKACCGoogle Scholar
  4. 4.
    Liying J, Xinxin F, Jianguo C, Zhonghai L (2012) Fault detection of rolling element bearing based on principal component analysis. In: Control and decision conference (CCDC), 2012 24th ChineseGoogle Scholar
  5. 5.
    Mujica L, Rodellar J, Fernandez A, Guemes A (2011) Q-statistic and T2-statistic PCA-based measures for damage assessment in structures. Struct Health Monit 10(5):539–553CrossRefGoogle Scholar
  6. 6.
    Tao EP, Shen WH, Liu TL, Chen XQ (2013) Fault diagnosis based on PCA for sensors of laboratorial wastewater treatment process. Chemometr Intell Lab Syst 128:49–55CrossRefGoogle Scholar
  7. 7.
    Liying J, Baojian X, Jianhui X, Jianguo C, Li F (2012) Improved confidence limits of T2 statistic for monitoring batch processes. In: Control and decision conference (CCDC), 2012 24th ChineseGoogle Scholar
  8. 8.
    MacGregor JF, Kourti T (1995) Statistical process control of multivariate processes. Control Eng Pract 3(3):403–414CrossRefGoogle Scholar
  9. 9.
    Mallat S (1989) A wavelet tour of signal processing. Academic Press, San DiegoGoogle Scholar
  10. 10.
    Muralidharan V, Sugumaran V (2012) A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Appl Soft Comput 12(8):2023–2029CrossRefGoogle Scholar
  11. 11.
    Latuny J, Entwistle R (2010) Bearing fault analysis through the application of ANFIS and vector array indicators based on statistical parameters of wavelet transformation component. In: Australasian congress on applied mechanics (ACAM 6) conferenceGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Berli Kamiel
    • 1
    • 2
  • Kris McKee
    • 2
  • Rodney Entwistle
    • 2
  • Ilyas Mazhar
    • 2
  • Ian Howard
    • 2
  1. 1.Department of Mechanical EngineeringUniversitas Muhammadiyah YogyakartaYogyakartaIndonesia
  2. 2.Department of Mechanical EngineeringCurtin UniversityBentleyAustralia

Personalised recommendations