Denoising with advanced stepwise false discovery rate control and its application to fault diagnosis

To extract defect features from the signal with background noise for fault diagnosis, a novel approach is proposed by using advanced false discovery rate procedure (AFDR). The main idea is based on controlling false discovery rate (FDR) through combination of all three stepwise procedures (step-up, step-down, step-up-down) and estimation of the number of true null hypotheses. The AFDR procedure differs from the standard FDR procedure in two respects, i.e., enhancing the efficiency by reducing the number of tested hypotheses and improving the power. The proposed procedure consists of two main steps: firstly, the signal is represented more parsimoniously in wavelet domain; secondly, a most appropriate stepwise FDR procedure is selected according to the character of wavelet coefficients. Both the numerical simulation results and the experimental results for bearing defect diagnosis show that the proposed approach is a competitive shrinkage method compared with other popular techniques.

► We propose a novel approach to fault diagnosis using advanced false discovery rate procedure (AFDR).
► The procedure controls false discovery rate (FDR) through combination of all three stepwise procedures.
► The startpoint of the test is determined by the number of true null hypotheses.
► The result of application to bearing defect testing verifies the proposed approach.