Dictionary　learning　has　emerged　as　an　effective　approach　for　data-driven　fault　diagnosis　due　to　its　strong　sparse　representation　ability.　Nevertheless,　the　gathered　vibration　signals　always　exhibit　a　time-shift　phenomenon,　greatly　diminishing　the　representation　capability　of　dictionary　learning　methods.　Besides,　in　real-world　industrial　scenarios,　where　heavy　noise　as　common　features　inevitably　cover　the　discriminative　features,　the　decline　in　the　recognition　performance　of　data-　driven　fault　diagnosis　methods　is　a　critical　challenge.　In　this　paper,　a　novel　weighted　sparse　classification　framework　with　extended　discriminative　dictionary　(WSC-EDD)　is　proposed.　First,　an　extended　discriminative　dictionary　design　strategy　is　developed　to　construct　generalized-　domain　extended　dictionary　model　with　strong　representation　and　discrimination　ability,　in　which　a　novel　generalized-domain　dictionary　fusion　method　is　developed　to　reduce　the　effect　of　time-shift　phenomenon　and　enhance　the　representation　ability　of　the　dictionary.　Further,　generalized-domain　discriminative　sub-dictionaries　are　optimized　by　the　K-singular　value　decomposition　in　a　data-driven　fashion　and　then　the　whole　extension　dictionary　are　designed　adaptively.　Second,　a　weighted　optimization　method　is　developed　to　highlight　the　contribution　of　non-zero　elements　in　the　correct　projection　region　in　sparse　codes,　in　which　weighted　diagonal　matrices　are　designed.　Finally,　a　sparse　recognition　method　is　established　for　bearing　fault　classification.　The　experiment　results　on　three　challenging　bearing　datasets　demonstrate　that　the　proposed　framework　yields　average　diagnosis　accuracies　of　99.75%,　99.93%　and　100%,　respectively.　Moreover,　the　superiority　and　robustness　of　WSC-EDD　are　further　confirmed　by　comparing　with　several　competing　methods.