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Decision support system using artificial immune recognition system for fault classification of centrifugal pump

Author

Listed:
  • N.R. Sakthivel
  • Binoy B. Nair
  • V. Sugumaran
  • Rajakumar S. Rai

Abstract

Centrifugal pumps are a crucial part of many industrial plants. Early detection of faults in pumps can increase their reliability, reduce energy consumption, service and maintenance costs, and increase their life-cycle and safety, thus resulting in a significant reduction in life-time costs. Vibration analysis is a very popular tool for condition monitoring of machinery like pumps, turbines and compressors. The proposed method is based on a novel immune inspired supervised learning algorithm which is known as artificial immune recognition system (AIRS). This paper compares the fault classification efficiency of AIRS with hybrid systems such as principle component analysis (PCA)-Naive Bayes and PCA-Bayes Net. The robustness of the proposed method is examined using its classification accuracy and kappa statistics. It is observed that the AIRS-based system outperforms the other two methods considered in the present study.

Suggested Citation

  • N.R. Sakthivel & Binoy B. Nair & V. Sugumaran & Rajakumar S. Rai, 2011. "Decision support system using artificial immune recognition system for fault classification of centrifugal pump," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 3(1), pages 66-84.
    • Handle: RePEc:ids:injdan:v:3:y:2011:i:1:p:66-84
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    Cited by:

    1. Galina Samigulina & Zarina Samigulina, 2022. "Diagnostics of industrial equipment and faults prediction based on modified algorithms of artificial immune systems," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1433-1450, June.

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