Period-refined CYCBD using time synchronous averaging for the feature extraction of bearing fault under heavy noise

• DOI: 10.1177/14759217231181514
• Publication Date: 2023-07-04T06:33:50Z
• Authors: Yonghao Miao; Huifang Shi; Chenhui Li; Jiadong Hua; Jing Lin
• Keywords: Rolling-element bearing; SIGNAL (programming language)

Deconvolution methods have been widely used in machinery fault diagnosis. However, their application would be confined due to the heavy noise and complex interference since feature measured signal becomes rather weak. Time synchronous averaging (TSA) can enhance periodic components suppress others by comb filter function. And iteration process of deconvolution methods, filtered after each further processed using TSA, time delay with maximum Gini index value is refined as iterative period for next iteration. Benefitting from these advantages, a period-refined second-order cyclostationarity blind (PRCYCBD) TSA proposed weak detection rolling element bearings (REBs) this paper. Firstly, without any prior knowledge, method which estimate more accurately suitable REBs, especially incipient fault. Secondly, firstly applied than just depending on Signal Noise Ratio (SNR) . Furthermore, new improvement frame expanded other algorithms, under noise. Finally, simulation slight bearing well two real experimental data including vibration wind turbine acoustical locomotive wheel verify superiority PRCYCBD compared traditional minimum entropy autocorrelation-improved deconvolution.