In　the　real　chiller　fault　diagnosis　tasks,　the　training　data　of　target　faults　is　commonly　unavailable.　This　scenario　restricts　the　diagnostic　performance　of　data-driven　methods.　The　existing　zero-shot　learning-based　diagnostic　models　have　conquered　this　challenge　by　introducing　sparse　fault　semantic　attributes.　However,　the　sparse　semantic　representation　misses　the　interconnections　between　attributes,　which　causes　difficulty　in　representing　holistically　the　latent　associations　between　categories.　In　this　article,　an　innovative　zero-shot　fault　diagnosis　(ZSFD)　framework　for　chillers　based　on　sentence-level　text　attributes　is　proposed　to　overcome　this　difficulty.　The　defined　fault　attributes　contain　two　textual　labels　for　the　cause　and　consequence.　Subsequently,　all　text　attributes　for　each　category　are　combined　into　a　sentence　(in　text　form),　which　is　named　sentence-level　text　attributes.　Afterward,　a　semantic　representation　model,　namely,　sentence-level　bidirectional　encoder　representations　from　transformer　(SBERT),　is　employed　to　project　sentence-level　text　attributes　as　the　dense　semantic　attributes.　Later,　a　cross-modal　contrastive　embedding　(CMCE)　model　is　established　to　embed　semantic　attributes　and　collected　sensor　data　into　a　common　latent　space,　where　the　distributions　of　attributes　and　data　are　aligned.　Meanwhile,　a　hybrid　reconstruction　classification　strategy　is　designed　in　CMCE　to　fully　integrate　the　intrinsic　characteristics　of　categories　from　both　modalities.　Finally,　the　unseen　semantic　attributes　embedded　by　the　CMCE　are　employed　for　training　zero-shot　learning　classifier.　Extensive　experiments　are　designed　and　executed　on　the　chiller　dataset.　The　results　demonstrate　that　the　proposed　framework　can　improve　the　distribution　consistency　between　data　and　attributes　compared　with　the　ZSFD　methods　based　on　sparse　representation,　and　achieves　satisfactory　diagnostic　performance.