An adjustable envelope based EMD method for rolling bearing fault diagnosis

Abstract

Abstract Empirical mode decomposition (EMD) can decompose complex non-stationary signals into the sum of several intrinsic mode functions (IMF), and it is widely used in fault diagnosis of mechanical devices. In order to solve the mode mixing problem and enhance the fault feature extraction ability, an adjustable envelope based EMD (AE-EMD) method was proposed. Firstly, the envelope fitting method is replaced by rational Hermite interpolation, which can effectively avoid the outstanding over and undershoot problem in the conventional fitting curve. Secondly, the optimal parameter in the AE-EMD is searched by grey wolf optimizer (GWO) using the maximum Kurtosis index as the objective function. Then, the AE-EMD method combined with the Hilbert envelope spectrum is employed to extract the fault characteristic frequency. Case study demonstrates that AE-EMD can restrain the mode mixing effectively, and it also has better fault feature extraction ability compared with the conventional EMD. This proposed method has potential significance to the fault diagnosis and condition-based maintenance (CBM) of rolling bearings in the large and complex equipment.