Intelligent Identification of Cavitation State of Centrifugal Pump Based on Support Vector Machine

Xiaoke He, Yu Song, Kaipeng Wu, Asad Ali, Chunhao Shen and Qiaorui Si
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Xiaoke He: School of Energy and Power Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Yu Song: School of Energy and Power Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Kaipeng Wu: Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China
Asad Ali: Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China
Chunhao Shen: Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China
Qiaorui Si: Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China

Energies, 2022, vol. 15, issue 23, 1-17

Abstract: In order to perform intelligent identification of the various stages of cavitation development, a micro high-speed centrifugal pump was used as a research object for vibration signal analysis and feature extraction for normal, incipient cavitation, cavitation and severely cavitated operating states of the pump at different temperatures (25 °C, 50 °C and 70 °C), based on support vector machines to classify and identify the eigenvalues in different cavitation states. The results of the study showed that the highest recognition rate of the individual eigenvalues of the time domain signals, followed by time frequency domain signals and finally frequency domain signals, was achieved in the binary classification of whether cavitation occurred or not. In the multi-classification recognition of the cavitation state, the eigenvalues of the time domain signals of the four monitoring points, the time frequency domain signals of the monitoring points in the X-direction of the inlet pipe and the Y-direction of the inlet pipe are combined, and the combined eigenvalues can achieve a multi-classification recognition rate of more than 94% for the cavitation state at different temperatures, which is highly accurate for the recognition of the cavitation state of centrifugal pumps.

Keywords: support vector machines; feature extraction; cavitation monitoring; cavitation state recognition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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