Fault　diagnosis　aims　to　accurately　and　timely　identify　and　locate　equipment　faults　to　ensure　the　safe　and　steady　functioning　of　systems.　The　rise　of　multimodal　data　has　led　to　widespread　interest　in　the　domain　of　fault　diagnosis　for　multimodal　fusion　technology.　However,　due　to　the　complex　network　structure　and　high　computational　cost,　it　is　difficult　to　learn　features,　and　the　interaction　of　different　modalities　is　not　sufficient,　resulting　in　unsatisfactory　fault　diagnosis　accuracy.　To　address　the　issue　of　low　accuracy　in　fault　diagnosis　of　multimodal　information　fusion,　a　novel　fault　diagnosis　method　based　on　feature　fusion　is　introduced.　Firstly,　one-dimensional　Convolutional　Neural　Network　(CNN)　is　employed　to　extract　the　features　of　vibration　signals,　so　as　to　obtain　the　features　in　time-domain　and　frequency-domain　respectively.　Secondly,　in　order　to　enrich　the　feature　information　and　simplify　the　network　structure,　the　features　in　time-domain　and　frequency-domain　are　activated　and　reconstructed　respectively　to　convert　them　into　time-domain　maps　and　spectrograms　and　synthesize　composite　images.　Finally,　the　composite　image　is　input　into　the　CNN　to　complete　the　fault　classification　task.　The　outcomes　demonstrate　that　the　suggested　approach　achieves　a　high　fault　diagnosis　accuracy　without　escalating　the　complexity　of　the　network　structure.