Convolutional　neural　networks　(CNNs)　have　weight-sharing　and　feature-learning　abilities,　and　can　efficiently　and　effectively　be　used　for　the　health　monitoring　of　industrial　equipment.　However,　the　pooling　operation　in　a　typical　CNN　can　cause　the　loss　of　valuable　impulse　features　during　data　down-sampling.　We　propose　grouping　sparse　filtering　(GSF)　to　overcome　this　problem.　Instead　of　using　a　pooling　operation,　the　GSF　splits　the　channels　of　features　obtained　after　convolution　into　equal-length　groups.　A　feature　selector　with　a　feature　aggregation　function　based　on　the　channel　importance　factors　and　a　lasso　constraint　is　used　to　filter　the　groups　to　perform　down-sampling.　The　GSF　method　preserves　the　impulse　features　due　to　the　block　sparsity　of　the　vibration　signal.　Theoretical　analysis　demonstrates　that　the　GSF　has　a　similar　computational　complexity　to　using　a　pooling　layer　in　a　CNN　for　the　same　number　of　layers.　Two　experimental　studies　were　conducted　using　data　from　a　laboratory　test　and　industrial　environments.　The　experimental　results　show　that　the　1D-CNN　with　GSF　provides　better　performance　for　retaining　the　impulse　features　of　the　rotating　machinery　signals　and　higher　fault　identification　accuracy　than　a　CNN　with　a　pooling　layer.