Research on Fault Diagnoses of Rolling Bearing Based on the Energy Operator Demodulation Approach

Abstract

This paper will introduce and optimize a new model of demodulation algorithm named Energy Operator Demodulation Approach to improve the limitation of the demodulation analysis widely used in many fields at present. Compared with Hilbert transform method, and optimized algorithm through Simulation test, which shows optimized Energy Operator algorithm is more simple ,faster and accurate than other algorithms. Finally, it is verified that research on fault diagnoses of rolling bearing based on the energy operator demodulation approach is very effective.

Rolling Bearing Incipient Fault Detection Based on a Multi-Resolution Singular Value Decomposition

“…In order to extract the fault feature information from vibration signals, various signal processing methods have been used in the field of fault detection [7,8], such as empirical mode decomposition (EMD) [9], wavelet transform (WT) [10], and their improved methods [11][12][13][14], sparse decomposition [15,16], manifold learning [17], deep learning [18], spectral kurtosis(SK) [19], and envelope analysis [20][21][22][23][24]. These methods realize fault detection from the time domain, frequency domain, or time-frequency domain.…”