Fault　diagnosis　based　on　single-modal　features　struggles　to　capture　the　coupling　relationship　between　multiple　fault　factors,　resulting　in　inferior　diagnosis　accuracy.　To　address　this　problem,　a　transfer　learning-based　multimodal　feature　fusion　(TL-MMFF)　model　is　proposed　for　fault　diagnosis.　First,　a　continuous　wavelet　transform　(CWT)-based　modal　expression　method　is　employed　to　transform　raw　vibration　signals　into　time-frequency　representations.　Then,　this　high-resolution　time-frequency　modal　can　be　utilized　to　capture　transient　vibration　and　energy　changes　in　nonstationary　signals.　Second,　a　multimodal　feature　fusion　strategy　is　proposed,　which　designs　learnable　parameters　to　dynamically　weight　the　time-domain　features　of　torque　and　the　time-frequency　features　of　vibration　signals.　This　adaptive　weighting　strategy　optimizes　the　fusion　process　based　on　the　correlation　of　different　modal　feature　sets,　thereby　enhancing　the　ability　to　describe　fault　characteristics.　Third,　a　maximum　mean　discrepancy　(MMD)-based　transfer　learning　(TL)　algorithm　is　designed　to　reduce　the　distribution　differences　between　fused　features　under　different　operating　conditions.　Then,　the　model　can　identify　fault　characteristics　across　varying　operating　conditions.　Finally,　experiments　on　the　Paderborn　University　dataset　demonstrate　that　TL-MMFF　achieves　99.1%　accuracy　and　converges　30%　faster　than　single-modal　methods.　These　results　validate　the　effectiveness　of　the　model　in　integrating　multimodal　data　and　generalizing　across　domains.