Research on Common Fault Diagnosis and Classification Method of Centrifugal Pump Based on ReliefF and SVM

Xingxin Xiao; Hui Chen; Liang Dong; Houlin Liu; Chuanhan Fan

doi:10.5293/IJFMS.2022.15.2.287

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자료유형: 학술저널

저자정보: Xingxin Xiao (Jiangsu University) Hui Chen (Xi’an Aerospace Propulsion Institute) Liang Dong (Jiangsu University) Houlin Liu (Jiangsu University) Chuanhan Fan (Jiangsu University)

저널정보: 한국유체기계학회 International Journal of Fluid Machinery and Systems International Journal of Fluid Machinery and Systems Vol.15 No.2

발행연도: 2022.6

수록면: 287 - 296 (10page)

DOI: 10.5293/IJFMS.2022.15.2.287

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The centrifugal pump is an important rotating machine and it is very critical to identify and differentiate among its common faults as quickly and accurately as possible. Based on the ReliefF algorithm and the sparrow search algorithm (SSA) in conjunction with support vector machine (SVM), an approach for faults classification and diagnosis of centrifugal pumps is proposed, its advantages over traditional fault diagnosis methods include a reduction in the number of characteristic parameters, shorter diagnosis times, as well as improved classification accuracy and robustness. We collected the fault data by designing a centrifugal pump fault test bench that recorded vibration signals for the rotor misalignment fault, the rotor unbalance fault, the seal ring wear fault, and normal operating conditions, and preprocessed the collected signals with Kalman filtering to remove noise interference, the time domain characteristic indexes and the frequency domain characteristic indexes of the filtered signal were extracted, each feature index is given a distinct weight using the ReliefF method, and the eigenvalues with weights less than the threshold are deleted, and the feature indexes that remain create a defect feature matrix. Particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing algorithm (SA) were used to optimize the SVM for comparison in order to verify the SSA-SVM model’s performance for fault diagnosis. The comparison results show that the model has high recognition accuracy, short Classification time, and strong robustness.

#centrifugal pump #fault diagnosis #ReliefF algorithm #SSA algorithm #SVM

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