A Novel Fault Diagnosis Method Based on Noise-Assisted MEMD and Functional Neural Fuzzy Network for Rolling Element Bearings

To solve the problem in which the auxiliary white-noise parameters need to be artificially selected in a noise-assisted multivariate empirical mode decomposition (NA-MEMD) and considering the fact that obtaining a large number of typical fault samples in practical engineering is difficult, a rolling bearing fault-diagnosis method based on velocity modified-mutation particle swarm optimization (PSO)-optimized NA-MEMD and improved functional neural fuzzy network (FNFN) is proposed. First, the original vibration signal is processed using the velocity modified-mutation PSO–NA-MEMD method to decompose it into a series of intrinsic mode functions (IMFs) with different characteristic time scales. Owing to the distribution characteristics of the IMF with the time scales and because the energy in different states of a bearing in different frequency bands changes, we calculate the energy moment of each IMF as a fault feature vector. Second, the fault feature vectors are then used as input to construct the improved FNFN structure. Finally, the method is validated using the data from the bearing data center of Case Western Reserve University and the vibration signals of the propulsion-motor bearings in the rudder paddle compartment during normal ship navigation and is compared with other neural network. The results show that the method proposed in this paper can quickly and more accurately diagnose rolling-bearing faults using limited training samples.