Intelligent fault diagnosis of rotating machine based on SVMs and EMD method

Empirical mode decomposition (EMD) is a self-adaptive analysis method for signal process. Because the EMD method is highly efficient in non-stationary and nonlinear data analysis. It has been widely applied to fault diagnosis of rotating machine. However, EMD method is not suitable for the Intelligent fault diagnosis, because the number of intrinsic mode functions (IMFs) is unfixed. In this paper, a classification method based on correlation coefficient was present, which can establish a one-on-one relationship between IMFs which decomposed from different signals by EMD method. And then, the feature of each IMFs is extracted and evaluated by using Support vector machines (SVMs). That will make the intelligent fault diagnosis possible. In order to prove the effectiveness of the method, the proposed method is applied to fault diagnosis on the signals get from a test rig.