CFFsBD: A Candidate Fault Frequencies-Based Blind Deconvolution for Rolling Element Bearings Fault Feature Enhancement

The repetitive transient impulses are typical symptoms of rolling bearing faults. The indicator of second-order cyclostationarity (ICS2)-based blind deconvolution (CYCBD) maximizing the cyclostationary behavior of the excitation source proves to be an effective method for enhancing the periodic cyclostationarity component caused by bearing defect. However, the deviation of the fault characteristic frequency-a common phenomenon easily caused by roller creep or measurement systems, etc., can lead to the collapse of this method in practical applications. Additionally, this method is prone to induce the generation of pseudo-cyclostationary components under the guidance of an inappropriate cyclic frequency. Thus, this work proposes a candidate fault frequencies (CFFs)-based blind deconvolution, abbreviated as CFFsBD, for fault feature enhancement of bearings. This idea comes from the concept of CFFs-a collection of frequencies most likely to be associated with bearing fault that can be identified by mining the local time-frequency features of the signal to be analyzed. A new indicator constructed by using CFFs to replace the cycle frequencies in ICS2 is utilized as the criterion to guide the solution of the deconvolution filter. This new indicator is a generalized version of ICS2, and thus, CYCBD can be considered a special case of the proposed CFFsBD. The performance of the CFFsBD is demonstrated by the analysis of simulated and experimental signals of faulty bearings. The results highlight the robustness and stability of the proposed CFFsBD in extracting fault-induced cyclostationary symptoms.

Automated summary

A candidate fault frequencies-based blind deconvolution method (CFFsBD) is proposed for enhancing the periodic cyclostationary component caused by bearing defects. This method uses the concept of candidate fault frequencies, which are frequencies most likely to be associated with bearing faults, to guide the solution of the deconvolution filter. The CFFsBD demonstrates robustness and stability in extracting fault-induced cyclostationary symptoms.