An Adaptive Spectrum Segmentation Method to Optimize Empirical Wavelet Transform for Rolling Bearings Fault Diagnosis

Abstract

Bearings play an important role in the industrial system. Among all kinds of diagnosis methods, empirical wavelet transform has been widely used for its characteristic of separating empirical modes from the spectrum. Although the empirical wavelet transform restrains the mode aliasing of extracting modal functions from the time domain, it runs slowly and separates a large number of invalid components. In this paper, an adaptive and fast empirical wavelet transform method is proposed. The method extracts the first feature cluster in the Fourier transform of the spectrum to reconstruct the trend spectrum. The minimum points are regarded as the initial boundaries. The key spectral negentropy is proposed to extract the frequency band which may contain main information. This method reduces the number of invalid components and computation time by filtering components before reconstruction. The simulation signal proves that the proposed method is effective and the proposed key spectral negentropy has stronger anti-noise ability than kurtosis. The experimental signals show that the method can successfully extract the fault features of inner or outer rings of bearings, and is suitable for the diagnosis of composite faults.