The Traverse Symplectic Correlation-Gram (TSCgram): A New and Effective Method of Optimal Demodulation Band Selection for Rolling Bearing

Abstract

Resonance demodulation is a commonly used method to obtain fault information, whose main challenge is to seek a suitable frequency band for demodulation. Autogram is a recently proposed method for optimal demodulation frequency band (ODFB) selection, in which the frequency domain is divided by a binary tree structure. However, the frequency band information obtained by Autogram is easily missed. To solve this issue, a 1/3 binary tree structure is employed to segment the frequency domain and improve the segmentation accuracy of Autogram. Despite this, the frequency bands with different position information cannot be fully obtained through the above-mentioned structure yet. In this article, the traverse symplectic correlation-gram (TSCgram) is proposed to overcome the shortcomings existing in Autogram and based on that, a novel fault diagnosis method for rolling bearing is proposed. First, the frequency domain of the raw vibration signal of rolling bearing is accurately divided through the traversal segmentation structure. Then, the concept of symplectic correlation is introduced to reduce the interference of noise, as well as other unrelated components on the traditional kurtosis, thus further improving the accuracy of ODFB detection. Finally, the band position corresponding to the maximum symplectic correlation kurtosis (SCK) value is chosen as the ODFB for fault diagnostics of rolling bearing. Also, the proposed method is applied to the simulated and measured data analysis by comparing it with fast kurtogram and Autogram methods. The comparison and analysis results indicate that the fault diagnostic effect of the proposed TSCgram method is better than the comparing methods.