It　is　a　challenging　problem　to　design　excellent　dictionaries　to　sparsely　represent　diverse　fault　information　and　simultaneously　discriminate　different　fault　sources.　Therefore,　this　paper　describes　and　analyzes　a　novel　multiple　feature　recognition　framework　which　incorporates　the　tight　frame　learning　technique　with　an　adaptive　subspace　recognition　strategy.　The　proposed　framework　consists　of　four　stages.　Firstly,　by　introducing　the　tight　frame　constraint　into　the　popular　dictionary　learning　model,　the　proposed　tight　frame　learning　model　could　be　formulated　as　a　nonconvex　optimization　problem　which　can　be　solved　by　alternatively　implementing　hard　thresholding　operation　and　singular　value　decomposition.　Secondly,　the　noises　are　effectively　eliminated　through　transform　sparse　coding　techniques.　Thirdly,　the　denoised　signal　is　decoupled　into　discriminative　feature　subspaces　by　each　tight　frame　filter.　Finally,　in　guidance　of　elaborately　designed　fault　related　sensitive　indexes,　latent　fault　feature　subspaces　can　be　adaptively　recognized　and　multiple　faults　are　diagnosed　simultaneously.　Extensive　numerical　experiments　are　sequently　implemented　to　investigate　the　sparsifying　capability　of　the　learned　tight　frame　as　well　as　its　comprehensive　denoising　performance.　Most　importantly,　the　feasibility　and　superiority　of　the　proposed　framework　is　verified　through　performing　multiple　fault　diagnosis　of　motor　bearings.　Compared　with　the　state-of-the-art　fault　detection　techniques,　some　important　advantages　have　been　observed:　firstly,　the　proposed　framework　incorporates　the　physical　prior　with　the　data-driven　strategy　and　naturally　multiple　fault　feature　with　similar　oscillation　morphology　can　be　adaptively　decoupled.　Secondly,　the　tight　frame　dictionary　directly　learned　from　the　noisy　observation　can　significantly　promote　the　sparsity　of　fault　features　compared　to　analytical　tight　frames.　Thirdly,　a　satisfactory　complete　signal　space　description　property　is　guaranteed　and　thus　weak　feature　leakage　problem　is　avoided　compared　to　typical　learning　methods.　(C)　2017　Elsevier　Ltd.　All　rights　reserved.