Fault Diagnosis of Rolling Bearing Based on EEMD-Hilbert and FWA-SVM

To effectively extract the non-stationary characteristics of the rolling bearing vibration signal and improve the fault diagnosis efficiency, a feature extraction method based on the ensemble empirical mode decomposition (EEMD) and Hilbert transform was proposed. The support vector machine (SVM) classification parameters were optimised using the fireworks algorithm (FWA) for the rolling bearing fault diagnosis method. The EEMD method was used to decompose the target signal into several modal functions. The instantaneous frequencies of the modal functions were obtained through Hilbert transforms. Statistical feature extraction and dimensionality reduction were respectively performed for the modal function and instantaneous frequency. The fireworks algorithm model was used to optimise the SVM parameters as well as the multi-classification fault diagnosis with training and test sets drawn from 600 datasets. The accuracy of the signal is estimated to be 99.633%, which is 0.4% and 0.2% higher than that of the traditional genetic algorithm and particle swarm optimisation algorithm, respectively. Further, the ability of iterative convergence is also seen to have obvious advantages. The feasibility and validity of the algorithm models are thus verified.