Fault Diagnosis of Fan Bearing Combined CEEMD and AO-SVM
Because of the bad operating environment of the fan,when the bearing is faulty,its vibration signal is often disturbed by environmental noise,which leads to the difficulty of fault information extraction for vibration signal.To solve this problem,this paper proposes a feature extraction method based on complementary ensemble empirical mode decomposition(CEEMD)and sample entropy(SE),which combines the Tianying optimization algorithm(AO)and support vector machine(SVM)for fault classification,and realizes the fault diagnosis of fan bearings.In this paper,the bearing data of Case Western Reserve University are used for the experiment,and the real fan bearing data are used for further verification.The experimental results show that the proposed method has high fault identification accuracy when fault vibration signal is disturbed by environmental noise.
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