Optimized LMD method and its applications in rolling bearing fault diagnosis

The local mean decomposition (LMD) method can adaptively decompose a vibration acceleration signal into a series of product functions with different frequency characteristics. However, in the process of calculating local mean and interpolation, this method not only increases the computational complexity, but also produces serious modal aliasing. In order to improve the computational speed and reduce modal aliasing, a new method is proposed to optimize local mean decomposition. The proposed method uses an order statistics filter to estimate the upper and lower envelopes of the signal. The moving average method is replaced by the envelope mean method to calculate the local mean function and envelope function. Subsequently, spectral negentropy is used as the stopping criterion of iteration and to filter the component which may contain fault information. Strict termination conditions in LMD are replaced. The invalid components avoid decomposition and the computational efficiency is improved. The dependence on extreme points is reduced, so the endpoint effect is suppressed. The experimental results show that the proposed method is capable of identifying bearing faults and is suitable for the diagnosis of composite faults of rolling bearings.