Early Fault Diagnosis of Motor Bearing Based on ALIF-MPE-SVM Combined Algorithm
In order to improve the safe operation and stability efficiency of motor bearings,non-stationary signals were decom-posed adaptively by ALIF algorithm,and nonlinear fault signals were extracted from IMFs by MPE.The fault feature values af-ter dimensionality reduction of MPE were used to develop a mPE-SVM fault diagnosis technology.Then the validity of the algo-rithm is verified according to the motor bearing fault parameters obtained by the test.The results show that most of the fault infor-mation occurs in the first three IMF,and the proportion of principal components exceeds 80% .Therefore,the former three princi-pal components are used as characteristic quantities and substituted into MPE-SVM for training.Each group can accurately identify the fault damage,indicating that MPE as fault feature can meet the requirements of effectiveness.Alif-mpe has better classification performance than EMD-MPE,with lower standard deviation and stable classification state.This research can accu-rately identify different fault degrees of motor bearings,which has a good theoretical support for improving the fault diagnosis level of similar mechanical transmission equipment.
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