The　long-tailed　distribution　of　monitoring　data　poses　challenges　for　deep　learning-based　fault　diagnosis　(FD).　Recent　efforts　utilizing　supervised　contrastive　learning　(SCL)　and　reweighted　loss　have　made　progress,　but　have　overlooked　two　key　issues:　1)　prevailing　random　undersampling　introduces　sample　influence　bias　and　suboptimal　model　learning;　and　2)　focusing　only　on　improving　the　FD　average　accuracy　compromises　fundamental　fault　judgement　(FJ),　heightening　missed-detective　and　false-alarm　risks　unsuitable　for　real-world　deployment.　To　fill　these　research　gaps,　this　paper　proposes　a　granularity　knowledge-sharing　SCL　(GKSSCL)　framework　for　longtailed　FD,　encompassing　GKS　supervised　contrasting　and　GKS　classification　stages.　In　the　former,　normal　data　are　clustered　into　multiple　fine-grained　subclasses　that　are　similar　in　size　to　the　fault　categories　for　balanced　contrasting.　Moreover,　a　mixed-granularity　contrastive　loss　facilitates　knowledge　sharing　across　granularities.　In　the　latter,　FJ　and　FD　tasks　were　concurrently　trained　through　a　knowledge　graph-based　adaptive　sharing　strategy.　Experiments　on　two　fault　datasets　showed　that　the　GKSSCL　can　effectively　harness　all　normal　data,　eliminate　sample　influence　bias,　and　enhance　FD　precision　without　sacrificing　FJ　reliability.