A novel deconvolution technique, termed CYCBDβ blind deconvolution (CYCBDβ), has recently been proposed. Compared to CYCBD and other blind deconvolution (BD) methods, CYCBDβ demonstrates superior performance in extracting fault-induced weak cyclic impulses under both Gaussian and non-Gaussian noise conditions (e.g., random impulsive noise). However, a major challenge in applying CYCBDβ to real-world scenarios is the appropriate selection of several critical parameters, particularly the target cyclic frequency and filter length. This is primarily because the effectiveness of CYCBDβ is highly dependent on the accuracy of the provided period information and filter size. To overcome this limitation, this study proposes a parameter-adaptive version of CYCBDβ, referred to as PA-CYCBDβ. The proposed method integrates an effective tool, an envelope harmonic product spectrum, specifically designed to accurately estimate the true cyclic frequency. Following this, a newly developed optimization algorithm, the escape optimization algorithm, is employed to determine the optimal filter length. Compared to the original CYCBDβ, PA-CYCBDβ can detect weak impulse features embedded in raw vibration signals without requiring prior knowledge of the periodicity and filter length. The effectiveness and advantages of PA-CYCBDβ are validated through both simulated signals and experimental data collected from a reduction gearbox on a concrete mixer truck test platform.
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