The　mechanical　structure　or　transmission　path　between　fault　source　and　sensor　location　always　distorts　the　impulsive　signatures　of　machine　faults.　It　is　thus　an　important　task　to　estimate　the　desired　impulsive　feature　and　the　influence　of　transmission　path　simultaneously　from　the　noisy　observation　signals.　Therefore,　a　convolutional　sparse　learning　model　(ConvSLM)　is　proposed　to　perform　impulsive　feature　detection.　The　ConvSLM　directly　models　the　modulation　process　of　the　transmission　path　and　is　completely　different　from　the　indirect　inverse　filter　design　scheme　as　popular　deconvolution　techniques　adopted.　Meanwhile,　to　overcome　the　inherent　drawbacks　of　the　popular　Kurtosis　maximization　strategy,　the　sparse　structure　of　the　impulsive　feature　is　integrated　into　the　objective　function　of　the　ConvSLM.　Different　from　the　recently　developed　two-stage　solver,　a　new　iterative　algorithm　with　only　one　stage　is　also　developed　under　a　multiple-block　nonconvex　alternating　direction　method　of　multiplier　framework　to　cope　with　the　non-convexity　and　nonsmoothness　of　the　sparsity-regularized　objective　function,　which　not　only　reduces　the　algorithmic　complexity　but　also　has　a　convergence　guarantee.　Numerical　experiments　on　synthetic　data　and　test　results　corroborate　the　efficacy　of　the　advocated　approach.　Compared　with　the　state-of-the-art　blind　deconvolution　techniques,　the　ConvSLM＇s　superiority　is　sufficiently　verified　through　its　application　on　the　impulsive　detection　of　the　wind　turbine　gearbox　gear.