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Minimum correlated generalized Lp/Lq deconvolution (MCG-Lp/Lq-D) is an important tool to detect periodic impulses in vibration mixture. It is proved to be a more stable technique than maximum correlated kurtosis deconvolution (MCKD) to recover the fault impulse under strong noise conditions. However, MCG-Lp/Lq-D still has limitations. One of the necessary conditions for the success of MCG-Lp/Lq-D is to provide a precise period of fault. An imprecise prior period will lead to performance degradation or even failure of the method. Therefore, in this paper, a MCG-Lp/Lq-D with adaptive fault period estimation capability is proposed, adaptive minimum correlated generalized Lp/Lq deconvolution (AMCG-Lp/Lq-D). The proposed method uses the autocorrelation function of envelope signal to estimate the fault period adaptively in each iteration and then takes the estimated period as the input parameter of MCG-Lp/Lq-D for the next iteration optimization. The proposed method does not require precise prior fault period input, which greatly improves the fault recovery accuracy and application range of MCG-Lp/Lq-D. Eventually, simulated and experimental data verify the effectiveness and superiority of AMCG-Lp/Lq-D. [ABSTRACT FROM AUTHOR]

Since minimum correlated generalized Lp/Lq deconvolution (MCG-Lp/Lq-D) cannot simultaneously have a good sensing ability to repetitive fault impulses and a strong robustness to noise when processing low SNR signals, a new deconvolution method, minimum improved correlated generalized Lp/Lq deconvolution (MICG-Lp/Lq-D), is proposed in this article. The proposed method is designed based on a novel norm, the improved correlated generalized Lp/Lq (ICG-Lp/Lq) norm. The ICG-Lp/Lq norm not only has the low-order statistics form that is similar to the correlated generalized Lp/Lq (CG-Lp/Lq) norm used in MCG-Lp/Lq-D, but also has the high shift-order signal correlation construction similar to the correlated kurtosis (CK) norm used in maximum correlated kurtosis deconvolution (MCKD). This guarantees that the proposed norm is robust enough to strong noise and can improve its sensing ability for repetitive impulses simultaneously. This feature of the ICG-Lp/Lq norm ensures that MICG-Lp/Lq-D can suppress outliers and extract weak impacts when processing low SNR signals. Simulated and experimental case studies verify the performance of the proposed method. The results indicate that the proposed method is robust enough to strong noise and very sensitive to repetitive impulses simultaneously, which indeed overcomes the aforementioned defects of MCG-Lp/Lq-D. Therefore, MICG-Lp/Lq-D can be considered a promising tool for detecting repetitive impulses. [ABSTRACT FROM AUTHOR]

• A novel BD technique based on criterion defined by envelope spectrum is proposed. • The proposed method does not require any prior parameters and is robust to the impulsive noises. • The proposed method is superior to the traditional time-domain BD methods, including MED and SF-SLSN. Repetitive transients are typical symptoms of rotating faults and their extraction can be used to indicate early rotating faults. The main issue is to extract the cyclo-stationarity of repetitive transients which is insensitive to impulsive noises. The objective of traditional blind deconvolution methods is to recover impulsive signals from time domain. Considering the cyclo-stationarity of repetitive transients, a novel blind deconvolution (BD) technique, coined as prior-unknown blind deconvolution technique, is proposed to extract the cyclo-stationarity of repetitive transients. The proposed technique finds an inverse filter by minimizing an optimal criterion that is defined based on envelope spectrum (ES). Additionally, the proposed method does not require the prior knowledge of initialization for BD. Based on the proposed method and the generalized Lp/Lq norm, a demonstration about how to design a BD algorithm for extracting the cyclo-stationarity of repetitive transients is introduced. Results showed that the proposed method is superior to traditional time-domain BD algorithms in detection performance and its robustness. [ABSTRACT FROM AUTHOR]