Fault Diagnosis of Bearings using IG-SVM and EMD-SVD

This paper present new method based on adaptive diagnosis, which can self-adaptively diagnose faults of bearings under varying operation conditions without any prior information. For this purpose, a new method using information-geometric support vector machine (IG-SVM) based on Empirical Mode Decomposition and Singular Value Decomposition (EMD–SVD) is presented. Firstly, the vibration signal is decomposed to singular features by the EMD-SVD. Then, the IG-SVM, which uses information geometry to modify SVM in a data-dependent way, is employed for fault clustering. The results show that the proposed method has an efficient approach for fault diagnosis of bearings.