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Fault diagnosis method for horizontal centrifugal pump based on CEEMD and optimized KNN
Published:2023-01-30 author:YANG Bo, HUANG Qian, FU Qiang, et al. Browse: 1113 Check PDF documents
Fault diagnosis method for horizontal centrifugal pump 
based on CEEMD and optimized KNN


YANG Bo1,3, HUANG Qian2,3, FU Qiang1,3, ZHU Rong-sheng1,3


(1.National Research Center of Pumps, Jiangsu University, Zhenjiang 212001, China; 2.China Nuclear Power 

Engineering Co., Ltd., Beijing 100840, China; 3.Joint Laboratory of Intelligent Diagnosis Operation and 

Maintenance of Nuclear Power Pumps and Devices, Zhenjiang 212013, China)


Abstract: As the background noise content in the actual measurement of horizontal centrifugal pump was large, and the fault characteristics were often submerged, resulting in poor mechanical fault diagnosis effect. In order to obtain its operation state and fault diagnosis in current time and accurately, a mechanical fault diagnosis method of horizontal centrifugal pump based on complementary ensemble empirical mode decomposition (CEEMD)-optimized k nearest neighbor (KNN) was proposed. Firstly, the mechanical fault acceleration signal of horizontal centrifugal pump was collected, and the signal was decomposed once by CEEMD to obtain the intrinsicmode function (IMF). The correlation coefficient of IMF was obtained by the correlation coefficient method to determine the correlation component and uncorrelated component. Secondly, the uncorrelated component was processed by the improved wavelet threshold denoising method to extract the time-frequency fault features that could be analyzed by the reconstructed signal. Finally, a centrifugal pump experimental bench was built, and the above fault diagnosis methods were used to classify and diagnose the mechanical faults of the centrifugal pump.The results show that the signal-to-noise ratio (SNR) of the signal evaluation index after CEEMD noise reduction is 2.2571, which is 0.4381 higher than the original denoising method. After optimization, the accuracy of KNN classification for mechanical fault diagnosis of horizontal centrifugal pump can reach 96.7%, which can effectively identify faults and achieve the purpose of intelligent diagnosis.

Key words: vane pump; decomposition of fault signals; complementary ensemble empirical mode decomposition(CEEMD); improved wavelet threshold noise reduction; k nearest neighbor (KNN) algorithm classification; intrinsic mode function (IMF); correlation component /uncorrelated component
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