Multi-fault Condition Monitoring of Slurry Pump with Principle Component Analysis and Sequential Hypothesis Test

Author:

Chen Hanxin12ORCID,Huang Wenjian1,Huang Jinmin1,Cao Chenghao1,Yang Liu1,He Yibin12,Zeng Li12

Affiliation:

1. School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430073, P. R. China

2. Hubei Provincial Key Laboratory of Chemical Equipment, Intensification and Intrinsic Safety, Wuhan Institute of Technology, Wuhan 430073, P. R. China

Abstract

A new method about the multi-fault condition monitoring of slurry pump based on principal component analysis (PCA) and sequential probability ratio test (SPRT) is proposed. The method identifies the condition of the slurry pump by analyzing the vibration signal. The experimental model is established using the normal impeller and the faulty impellers where the collected vibration signals were preprocessed using wavelet packet transform (WPT). The characteristic parameters of the vibration signals are extracted by time domain signal analysis and the dimension of data was reduced by PCA. The principal components with the largest contribution rate are chosen as the inputted signal to SPRT to assess the proposed algorithm. The new methodology is reasonable and practical for the multi-fault diagnosis of slurry pump.

Funder

Special Major Project of the Ministry of Science and Technology of Hubei Province of China

Major project of Hubei Provincial Department of Education

Research foundation for the returned Overseas Chinese Scholars of State Education Ministry

National Natural Science Foundation of China

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

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