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

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.

[1]  Jinmin Huang,et al.  Finite Element Analysis Model on Ultrasonic Phased Array Technique for Material Defect Time of Flight Diffraction Detection , 2020 .

[2]  Liu Yang,et al.  Particle Swarm Optimization Algorithm with Mutation Operator for Particle Filter Noise Reduction in Mechanical Fault Diagnosis , 2020, Int. J. Pattern Recognit. Artif. Intell..

[3]  Liu Yang,et al.  Multi-fault Condition Monitoring of Slurry Pump with Principle Component Analysis and Sequential Hypothesis Test , 2020, Int. J. Pattern Recognit. Artif. Intell..

[4]  H X Chen,et al.  Particle swarm optimization particle filter denoising algorithm with mutation operator , 2019, IOP Conference Series: Materials Science and Engineering.

[5]  H. X. Chen,et al.  Semiconductivities of passive films formed on stainless steel bend under erosion-corrosion conditions , 2018, Corrosion Science.

[6]  Hanxin Chen,et al.  Multiple fault condition recognition of gearbox with sequential hypothesis test , 2013 .

[7]  Guangjie Han,et al.  A BP Neural Network Prediction Model Based on Dynamic Cuckoo Search Optimization Algorithm for Industrial Equipment Fault Prediction , 2019, IEEE Access.

[8]  Liu Yang,et al.  Fault diagnosis of gearbox based on RBF-PF and particle swarm optimization wavelet neural network , 2019, Neural Computing and Applications.

[9]  Yanjun Lu,et al.  Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network , 2013 .

[10]  Jing-Li Luo,et al.  Silver sulfide anchored on reduced graphene oxide as a high -performance catalyst for CO 2 electroreduction , 2018, Journal of Power Sources.