The　successful　applications　of　deep　transfer　learning　to　intelligent　fault　diagnosis　testify　to　a　positive　correlation　between　transferable　feature　similarity　and　knowledge　transferability　across　diagnostic　tasks.　This　correlation　makes　feature　similarity　possible　to　assess　diagnostic　knowledge　transferability.　Therefore,　researchers　have　attempted　various　measures　for　feature　similarity,　and　distance　metrics　have　been　adopted　as　an　objective　measure　for　feature　distribution　discrepancy.　However,　the　commonly　used　distance　metrics　cannot　address　the　joint　distribution　discrepancy　(JDD)　due　to　the　difficulty　in　fitting　conditional　distributions　of　target　domain　samples.　To　overcome　the　problem,　we　resort　to　explore　cluster-conditional　distributions　instead　and　propose　an　optimal　transport-embedded　joint　distribution　similarity　measure　(OT-JDSM)　that　is　implemented　in　two　steps.　First,　a　cluster-true　label　propagation　spreads　labels　from　a　small　number　of　labeled　target　domain　samples　to　the　whole.　Second,　the　JDD　of　transferable　features　is　produced　via　an　efficient　solution　of　optimal　transport.　OT-JDSM　is　demonstrated　on　synthetic　examples　and　144　transfer　diagnosis　tasks　that　are　created　by　public　and　private　bearing　datasets.　The　results　show　that　OT-JDSM　of　transferable　features　has　a　stronger　correlation　with　diagnostic　knowledge　transferability　than　other　distance　metrics.　Moreover,　the　OT-JDSM　gain　can　quantify　the　transfer　performance　of　diagnostic　models　on　tasks.