Traversal index enhanced-gram (TIEgram): A novel optimal demodulation frequency band selection method for rolling bearing fault diagnosis under non-stationary operating conditions

Abstract

It is very important to select the optimal demodulation frequency band (ODFB) of rolling bearing vibration signals for the most valuable fault information extraction and diagnosis. Fast kurtogram (FK) is an effective and most commonly used ODFB selection approach for bearing fault diagnosis, which generally is founded on the filter bank structure and short-time Fourier transform. Though the FK method can effectively detect the shock characteristics of frequency band signals, other useful characteristics related with failure of vibration signal will be ignored. In this paper, a novel ODFB selection method called traversal index enhanced-gram (TIEgram) is proposed for rolling bearing vibration signals. In the proposed TIEgram method, first of all, the traversal segmentation model is utilized to transfer the original signal into frequency domain for enhancing overall segmentation performance of traditional binary trees and 1/3 binary trees structure segmentation models. Then, a new weighted fusion indicator based on the kurtosis, correlation coefficient and spectral negative entropy is designed to select ODFB from the segmented results of traversal segmentation model, which can effectively solve the problem that different vibration signal characteristics cannot be fully detected by a single indicator. After that, an enhanced adaptive multi-scale weighted morphological filtering-based envelope spectrum is employed to demodulate the obtained frequency band for a much more accurate diagnosis effect of rolling bearing. Finally, the simulated and measured signals of rolling bearing under stationary and non-stationary operating conditions are respectively used to verify the feasibility and effectiveness of the proposed method with comparison of the existing FK, Autogram and infogram methods. The comparison analysis results show that TIEgram method can accurately identify the most useful fault information and shows better performance than existing methods.