Vibration　signal　analysis　has　become　one　of　the　important　methods　for　machinery　fault　diagnosis.　The　extraction　of　weak　fault　features　from　vibration　signals　with　heavy　background　noise　remains　a　challenging　problem.　In　this　article,　we　first　introduce　the　idea　of　algorithm-aware　sparsity-assisted　methods　for　fault　feature　enhancement,　which　extends　model-aware　sparsity-assisted　fault　diagnosis　and　allows　a　more　flexible　and　convenient　algorithm　design.　In　the　framework　of　algorithm-aware　methods,　we　define　the　generalized　structured　shrinkage　operators　and　construct　the　generalized　structured　shrinkage　algorithm　(GSSA)　to　overcome　the　disadvantages　of　l1-norm　regularization-based　fault　feature　enhancement　methods.　We　then　perform　a　series　of　simulation　studies　and　two　experimental　cases　to　verify　the　effectiveness　of　the　proposed　method.　In　addition,　comparisons　with　model-aware　methods,　including　basis　pursuit　denoising　and　windowed-group-lasso,　and　fast　kurtogram　further　verify　the　advantages　of　GSSA　for　weak　fault　feature　enhancement.　©　1963-2012　IEEE.