A Fault Feature Extraction Method of Motor Bearing Using Improved LCD

Abstract

Local characteristic-scale decomposition (LCD) is an adaptive decomposition method for non-stationary and nonlinear time-varying signals. In this paper, kernel mapping is used to replace random mapping in LCD, and a fault feature extraction method based on kernel local characteristic-scale decomposition-Hilbert envelope spectrum (KLCD-Hilbert) is proposed. This method first performs wavelet noise reduction on the bearing fault signal, and then uses the KLCD method to adaptively decompose the motor bearing vibration signal into several intrinsic scale components (ISC), the kernel function determines the number of ISCs. Finally, create the Hilbert envelope spectrum of each state vibration signal in turn, and input the extracted characteristic data into the structure of the extreme learning machine (ELM) to realize the fault identification of the motor bearing. The experimental results show that the KLCD-Hilbert envelope spectrum can better reflect the fault characteristics of the motor bearing than the time domain or frequency domain amplitude in the process of identifying the state of the motor bearing. Moreover, the KLCD method has a higher recognition rate than the local mean decomposition (LMD) and empirical mode decomposition (EMD) methods.