In　recent　years,　the　machine　learning　(ML)-based　percussion　method　has　gained　considerable　attention　as　a　cost-effective　and　user-friendly　non-destructive　testing　(NDT)　technique.　However,　traditional　ML　classification　methods　fail　to　identify　previously　unseen　fault　levels　that　are　not　included　in　the　training　dataset,　thereby　limiting　their　practical　applicability.　This　paper　proposes　a　zero-shot　pipeline　fault　detection　method　based　on　a　multi-attribute　learning　model　to　identify　unseen　fault　classes　without　requiring　their　direct　signal　samples　during　training.　In　this　method,　each　fault　category　is　represented　by　a　six-dimensional　attribute　vector　that　characterizes　its　unique　properties.　During　the　attribute　learning　phase,　a　multi-attribute　learning　model　is　constructed　by　integrating　a　one-dimensional　convolutional　neural　network　(1D-CNN)　with　a　bidirectional　long　short-term　memory　network　(BiLSTM)　to　predict　the　fault　attributes.　Fault　recognition　is　subsequently　achieved　using　a　Euclidean　distance-based　classifier,　which　categorizes　the　predicted　attribute　vectors　based　on　their　similarity　to　predefined　attribute　representations.　The　results　demonstrate　that　when　the　test　set　originates　from　previously　unseen　pipelines,　the　proposed　method　significantly　outperforms　other　approaches　in　terms　of　classification　performance,　exhibiting　superior　adaptability　and　robustness.　Importantly,　it　effectively　identifies　unseen　fault　severity,　overcoming　the　limitations　of　traditional　methods.　In　conclusion,　the　proposed　method　offers　an　innovative　solution　to　the　problem　of　data　scarcity　in　fault　diagnosis,　with　promising　potential　for　application　in　complex　industrial　environments.