Identification of Cavitation Phenomenon in Centrifugal Pump by Artificial Immune Network Method

Document Type : Research Article

Authors

1 PhD student/IUST

2 IUST

Abstract

Reduce the cost of unscheduled shutdown and enhance the reliability of systems, is one of the important goals for various industries that could be achieved by condition monitoring. Cavitation is a common phenomenon in centrifugal pumps which causes the damage and its true identification in early stage is too important. In this paper cavitation is identified by use of artificial immune net that is modeled on the function of the human immune system. For this purpose, after data collection by a laboratory setup and extraction of various features, feature selection and dimensions reduction were done by artificial immune method and then with artificial immune net method, the system condition was identified. Finally, the results of this study were compared with the principal component analysis method and the results of nonlinear supportive vector machine, multi-layer artificial neural network, K-means and fuzzy C-means clustering.

Keywords

Main Subjects

References

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Amirkabir Journal of Mechanical Engineering
Volume 52, Issue 3
June 2020
Pages 717-730

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