Blind deconvolution based on combined kurtosis indicator and its application for fault detection of rotating machinery

To effectively extract early fault features in rotating machinery signals contaminated by intense noise, this paper proposes a novel fault detection method based on feature mode decomposition (FMD) and a maximum correlated kurtosis combined L-Kurtosis deconvolution algorithm (MCK-LKD). The method innovatively introduces a combined kurtosis indicator named CK-LK and establishes the MCK-LKD algorithm by integrating the statistical characteristics of correlated kurtosis and L-Kurtosis, significantly improving the detection capability for fault impact components. Furthermore, a two-stage processing framework FMD-MCK-LKD is constructed, effectively combining the noise reduction advantages of FMD with the fault enhancement capabilities of the MCK-LKD algorithm. The proposed method is tested on both simulated and experimental signals, with comparative analysis conducted against other mainstream deconvolution algorithms. The results demonstrate that the signal decomposition and reconstruction method based on FMD, combined with the MCK-LKD algorithm, exhibits significant advantages and superior performance in early fault detection of rotating machinery.