Centrifugal Pump Impeller Health Diagnosis Based on Improved Particle Filter and BP Neural Network

  • Hanxin ChenEmail author
  • Lu Fang
  • Dongliang Fan
  • Guangyu Zhang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1146)


This paper proposes an improved particle filter (PF) algorithm for the denoising of fault signals to reduce the impact of noise on the centrifugal pump impeller fault diagnosis. This method is combined with BP (back propagation) neural network to propose a trouble diagnosis method for impeller of centrifugal pump. Selecting the normal impeller and three centrifugal pumps with different fault impellers as experimental models. The improved PF algorithm is used to denoise the experimental data, then the principal component analysis (PCA) method is used for optimizing and selecting the eigenvalues. Finally, the constructed BP neural network model is used for fault identification. The accuracy of the model was verified by a four-fold cross test. In order to objectively compare the advantages of the proposed BP neural network diagnosis method based on improved PF. In this paper, the experimental results are compared with the experimental results of BP neural network based on traditional PF and particle swarm optimization particle filter (PSO-PF) algorithm. The experiment results indicate that the BP neural network diagnosis method based on the improved PF algorithm is effective for the centrifugal pump impeller fault diagnosis and has higher diagnostic accuracy. This method has certain significance for the research of centrifugal pump impeller fault diagnosis method.


Fault diagnosis Particle filter Centrifugal pump BP neural network Principal component analysis 



This work was supported by the Special Major Project of the Ministry of Science and Technology of Hubei Province of China (Grant No. 2016AAA056), Major project of Hubei Provincial Department of Education (Z20101501) and the National Natural Science Foundation of China (Grant 51775390).

Hubei Provincial Key Laboratory of Chemical Equipment, Intensification and Intrinsic Safety


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Hanxin Chen
    • 1
    Email author
  • Lu Fang
    • 1
  • Dongliang Fan
    • 1
  • Guangyu Zhang
    • 1
  1. 1.School of Mechanical and Electrical EngineeringWuhan Institute of TechnologyWuhanChina

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