Fault diagnosis of gear cases based on entropy of singular values and support vector machine

In view of the non-stationary features of vibration signals of gear and the difficulty to obtain a large number of fault samples in practice, a fault diagnosis scheme based on empirical mode decomposition (EMD) entropy of singular values and support vector machine is put forward in this paper. Firstly, original acceleration vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs); the initial feature vector matrixes are formed by the intrinsic mode functions. Secondly, using the singular value decomposition technique to the vector matrixes, the singular values are obtained. Finally, the singular values serve as the fault characteristic vectors to be inputted to the support vector machine classifier and the work conditions and fault patterns are identified by the output of the classifier. Practical examples show that the diagnosis approach put forward in this paper can identify gear fault patterns effectively.