Research on Rolling Bearing Fault Diagnosis Method in Petroleum Machinery Based on ICEEMDAN and PSO-LSSVM
In view of the weak energy and sparse features of fatigue fault vibration signals of rolling bearings,a fault identification method combining improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and particle swarm optimization least-squares support vector machine(PSO-LSSVM)is proposed.The improved adaptive noise complete empirical mode algorithm is used to decompose different bearing fault signals into a series of inherent modal function(IMF)components;The compo-nent that can best represent the original signal state is selected according to the correlation core-variance contribution ratio criterion,and the singular spectrum entropy of the reconstructed component is calculated to form the feature vector;The extracted feature vec-tor set is input into the least square support vector machine classifier based on particle swarm optimization,which trains the model and identifies the fault mode.The accuracy and efficiency of the model are compared with that of the support vector machine(SVM)and least-squares support vector machine(LSSVM)classifier.The test results show that the method can effectively extract fault charac-teristics from rolling bearing fault signals,with an accuracy of 98.75％,which has certain reliability and practicability.
rolling bearingICEEMDAN decompositionsingular spectrum entropyPSO-LSSVMpattern recognition
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