Open AccessFault diagnosis of motor rolling bearing based on IMF sample entropy and particle swarm optimization SVM
Author(s) -
Lei Yang,
Qinghe Hu,
Shuang Zhang
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/461/1/012037
Subject(s) - particle swarm optimization , sample entropy , support vector machine , entropy (arrow of time) , pattern recognition (psychology) , hilbert–huang transform , vibration , bearing (navigation) , artificial intelligence , fault (geology) , computer science , feature vector , principal component analysis , algorithm , control theory (sociology) , engineering , physics , acoustics , computer vision , control (management) , filter (signal processing) , quantum mechanics , seismology , geology
Aiming at the problem that it is difficult to effectively identify rolling bearing faults, a method for motor rolling bearing faults based on IMF sample entropy and particle swarm optimization SVM (PSO-SVM) is proposed. Firstly, the complementary collective empirical mode decomposition (CEEMD) is applied to the adaptive decomposition of bearing vibration signals to obtain a group of Intrinsic Modal Functions (IMFs). Then the sample entropy of the IMF component containing the main fault feature information was calculated to obtain the sample entropy matrix of the signal component, which was input as the feature vector into the particle swarm optimization SVM for training and testing. Through the analysis of simulation and experimental data, the method has a high identification accuracy for fault types.