Blind cyclostationary fault feature extraction in rolling bearings: a dual adaptive filtering approach

Abstract

Extracting cyclostationary features from vibration signals is one of the most effective approaches in bearing fault diagnosis. However, current methods require prior knowledge of cycle-frequencies or other statistical information, which constrains their applicability across various scenarios. In this paper, we introduce a novel dual adaptive filtering method, incorporating cycle-frequency estimation to solve the existing problem. The method firstly employs an adaptive line enhancer (ALE) to isolate the first-order cyclostationary signal, thereby the cycle-frequencies can be effectively detected using an exhaustive estimation technique. Subsequently, an adaptive frequency-shift (FRESH) filter is further applied to extract the second-order cyclostationary features from the residual components. The proposed method successfully overcomes the challenge of separating cyclostationary signals without prior knowledge and can be tailored to real-time application scenarios. Besides, the approach distinguishes between the two cyclostationary signal types, effectively resolving any aliasing concerns inherent in their statistical characteristics. The effectiveness of the method is verified through simulation, experiments, and engineering data analysis. It is demonstrated that the method significantly enhances diagnostic accuracy and is more suitable for early fault diagnosis of rolling bearings by estimating spectral coherence on the extracted signals.