Practical periodic feature enhancement framework for the detection of acoustic-based bearing faults

Abstract This paper proposes a two-step fault diagnosis scheme to address the challenge of weak fault feature extraction in bearing fault diagnosis using acoustic data. A practical periodic feature enhancement framework (PPFEF) is presented for deconvolving fault features. The method employs signal unbiased autocorrelation to optimize the deconvolution unified filter framework developed by synthesizing existing methods. Fault shock enhancement is achieved without needing previous fault information. Furthermore, to improve the effectiveness of PPFEF in extracting fault shocks, this paper presents an enhanced symplectic geometry modal decomposition (ESGMD) method for prenoise reduction. Inspired by singular value decomposition theory and dimension reduction concepts, the covariance matrix and trajectory matrix with sufficient essential features are built to strengthen the demodulation capability. A covariance-based discrete entropy is produced to characterize the fault periodic features and identify fault-sensitive components in the ESGMD. The effectiveness and key properties of PPFEF and ESGMD are analyzed. Numerical simulations and experimental studies are conducted on ESGMD–PPFEF.