Optimized minimum generalized Lp/Lq deconvolution for recovering repetitive impacts from a vibration mixture

Enhancing repetitive transients is a key to bearing fault detection. Blind deconvolution aims to recover impulsive impacts from repetitive transients and their mixture. Nowadays, based on different optimization criteria, such as kurtosis, entropy, generalized Lp/Lq norm, etc., various deconvolution methods have been proposed for recovering repetitive impacts. Due to the non-convex of these optimization criteria, local optimal solutions to these optimization problems may be obtained frequently. In this paper, inspired by the fast realization of spectral kurtosis, e.g. the fast kurtogram, an optimized minimum generalized Lp/Lq deconvolution (OMGD) is proposed and investigated. The main idea of the proposed method utilizes the filters designed in the fast kurtogram to provide proper initializations for the minimum generalized Lp/Lq deconvolution (MGD). Hence, multiple local optimal solutions can be iteratively obtained. Then, the minimum of these local optimal solutions is used as a precise estimate to approximate the globally optimal solution of MGD. Results show that the proposed method has better deconvolution performance than MGD and fast kurtogram for enhancement of repetitive impacts caused by localized bearing faults.