Open AccessResearch on Bearing Fault Diagnosis of Submersible Pump Motor Based on LMD and SVDD
Author(s) -
Zhengtao Yuan,
Fang Song,
Renjie Dou
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/711/1/012041
Subject(s) - bearing (navigation) , submersible pump , fault (geology) , engineering , vibration , condition monitoring , support vector machine , induction motor , electric motor , automotive engineering , computer science , marine engineering , artificial intelligence , geology , mechanical engineering , acoustics , physics , electrical engineering , voltage , seismology
The motor is a key component of the submersible pump. The health of the motor would greatly affect the safety and efficiency of the submersible pump. The bearing fault is one of the most common faults in motors. Therefore, detection and diagnosis of bearing faults are essential in the condition monitoring of pumps. In this paper, the local average decomposition (LMD) method is used to analyze the bearing vibration signals of submersible pump motor and extract feature vectors. A fault diagnostic model is established by the support vector data description (SVDD) to determine whether the submersible pump motor is faulty. The developed model exhibits practical significance in condition monitoring of submersible pump motor bearings.