Improved Complexity Based on Time-Frequency Analysis in Bearing Quantitative Diagnosis

Abstract

This paper compares and analyzes the quantitative diagnosis methods based on Lempel-Ziv complexity for bearing fault, using continuous wavelet transform (CWT), Empirical Mode Decomposition (EMD) method, and wavelet packet method for decomposition of vibration signal measured by acceleration sensors, respectively. The kurtosis and entropy indices are also analyzed in order to select the optimum analysis area from the vibration signal. The variation trend of vibration signal Lempel-Ziv complexity of bearing inner race and outer race with the varying fault severity is analyzed and predicted based on the generation mechanism and characteristics of fault vibration signals. Experimental results show that it is suitable for observing the fault growing trend and severity by examining the value based on improved Lempel-Ziv complexity algorithm using continuous wavelet transform (CWT), Empirical Mode Decomposition (EMD) method, and wavelet packet method for signal decomposition, using kurtosis indices for optimal analysis area selection, and using the optimal weight coefficients for complexity calculation.