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Cavitation state identification of centrifugal pump based on CEEMD-DRSN

  • Cui Dai (School of Energy and Power Engineering, Jiangsu University) ;
  • Siyuan Hu (Research Center of Fluid Machinery Engineering and Technology, Jiangsu University) ;
  • Yuhang Zhang (Research Center of Fluid Machinery Engineering and Technology, Jiangsu University) ;
  • Zeyu Chen (Research Center of Fluid Machinery Engineering and Technology, Jiangsu University) ;
  • Liang Dong (Research Center of Fluid Machinery Engineering and Technology, Jiangsu University)
  • Received : 2022.09.07
  • Accepted : 2023.01.12
  • Published : 2023.04.25

Abstract

Centrifugal pumps are a crucial part of nuclear power plants, and their dependable and safe operation is crucial to the security of the entire facility. Cavitation will cause the centrifugal pump to violently vibration with the large number of vacuoles generated, which not only affect the hydraulic performance of the centrifugal pump but also cause structural damage to the impeller, seriously affecting the operational safety of nuclear power plants. A closed cavitation test bench of a centrifugal pump is constructed, and a method for precisely identifying the cavitation state is proposed based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Deep Residual Shrinkage Network (DRSN). First, we compared the cavitation sensitivity of pressure fluctuation, vibration, and liquid-borne noise and decomposed the liquid-borne noise by CEEMD to capture cavitation characteristics. The decomposition results are sent into a 12-layer deep residual shrinkage network (DRSN) for cavitation identification training. The results demonstrate that the liquid-borne noise signal is the most cavitation-sensitive signal, and the accuracy of CEEMD-DRSN to identify cavitation at different stages of centrifugal pumps arrives at 94.61%

Keywords

Acknowledgement

The authors would like to thank the financial support from National Natural Science Foundation of China (No. 52279087, 51879122), Zhenjiang key research and development plan (GY2017001, GY2018025),the Open Research Subject of Key Laboratory of Fluid and Power Machinery, Ministry of Education, Xi-hua University (szjj2017094, szjj2016068), Jiangsu University Young Talent training Program-Outstanding Young backbone Teacher, Program Development of Jiangsu Higher Education Institutions (PAPD), and Jiangsu top six talent summit project (GDZB-017).

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