Machine fault signature analysis by midpoint-based empirical mode decomposition

Abstract

The fault signature can be revealed by vibration analysis in machine fault detection and diagnosis. Empirical mode decomposition (EMD) is a self-adaptive method that can decompose a vibration signal into informative intrinsic mode functions (IMFs). This paper addresses the improvement of the weakness of the traditional EMD algorithm and presents a new midpoint-based EMD method for effective fault signature analysis of a rotating machine. In the proposed method, geometrical midpoints of successive extrema are employed to estimate the local mean of an analyzed signal. Signal decomposition is then self-adaptively performed to achieve IMFs through removal of the midpoint-based local means. The representative IMF containing fault information is selected for identifying the fault signature. The effectiveness of the proposed method was verified by means of simulation and an application to gear fault diagnosis. Results indicated that the midpoint-based EMD is valuable in improving fault signature analysis of the rotating machine in comparison with the traditional EMD method.