Bearing faults diagnostics based on hybrid LS-SVM and EMD method

• Weighted LS-SVM is taken as the preprocess of EMD to remove the noises.
• The end points of envelope curve in EMD are predicted with LS-SVM rolling forecast modeling method.
• The average envelope is smoothed with adaptive mapped LS-SVM to suppress mode-mix phenomenon.
• The performance of hybrid LS-SVM–EMD is verified in rolling bearing fault detection.

In this paper, a novel method that integrates the LS-SVM and Empirical Mode Decomposition (EMD) is proposed to improve the performance of conventional EMD. The analyzed signal is preprocessed with the weighted Least Squares Support Vector Machines (LS-SVM) to suppress the interference of high-frequency intermittent components and other non-Gaussian noises. The denoised signal is extended with LS-SVM rolling forecast modeling. Next, the linear function is used to construct upper and lower envelopes of the extrapolated data in order to determine the temporary mean envelope curve which is then smoothed with the adaptive mapped LS-SVM to obtain the local mean curve. Signal decomposition is self-adaptively performed to achieve IMFs through removal of the smoothed local mean curve. The representative IMF containing fault information is selected for demodulation analysis to identify the fault characteristics. The effectiveness of the proposed method is verified by means of simulations and applications to bearing fault diagnosis.