With　the　advancement　of　complex　system　diagnosis,　prediction,　and　health　management　technologies,　digital　twin　technology　has　become　a　prominent　research　area　in　the　fields　of　intelligent　manufacturing　and　system　operation　and　maintenance.　However,　due　to　the　high　complexity　of　practical　systems,　the　difficulty　of　data　acquisition,　and　the　low　accuracy　of　modeling　techniques,　current　digital　twin　modeling　suffers　from　low　accuracy,　and　the　generalization　ability　of　models　is　poor　when　applied　in　model　transfer.　To　address　this　issue,　a　novel　fault　diagnosis　method　is　proposed,　which　integrates　a　digital　twin　model　based　on　transfer　learning.　The　framework　introduces　an　innovative　approach　to　construct　multiple　digital　twin　models　using　both　mechanistic　and　data-driven　models.　The　mechanism　twin　constructs　a　universal　simulation　model　based　on　physical　equipment　and　updates　it　with　system　response　measurement　data.　The　data　twin　consists　of　a　high-dimensional　fully　connected-generative　adversarial　network　twin　for　extracting　deep　features　from　data　and　an　long-　and　short-term　memory　twin　for　extracting　time　series　features.　Subsequently,　transfer　learning　is　introduced　to　achieve　deep　fusion　in　the　multiple　digital　twins　system.　The　mechanism　twin　is　used　to　obtain　source　domain　samples　to　construct　a　diagnostic　network,　and　the　data　twin　is　used　to　extract　target　domain　features　to　correct　the　diagnostic　network,　thereby　improving　the　accuracy　and　reliability　of　fault　diagnosis.　Finally,　the　proposed　framework　is　applied　to　the　fault　diagnosis　of　triplex　pump　equipment.　The　accuracy　of　diagnosis　continuously　improves　as　the　system　is　updated　and　ultimately　reaches　89.28%,　demonstrating　the　effectiveness　of　the　algorithm　and　providing　a　novel　solution　for　the　generalization　limitations　of　current　digital　twin　models.