Identifying　fault　of　rotating　machinery　under　different　load　conditions　with　high　accuracy　is　a　remaining　challenge　for　vibration　signal　based　fault　diagnosis.　Aiming　at　this　challenge,　this　paper　proposes　a　comprehensive　strategy　of　combining　mixed　kernel-support　vector　machine　(MSVM)　with　grasshopper　optimisation　algorithm　(GOA)　to　identify　typical　faults　of　rotating　machinery　subject　to　different　load　levels.　The　basic　idea　of　the　proposed　strategy　can　be　summarized　as　the　following　three　steps.　Firstly,　a　feature　vector　that　uses　multi-domain　indexes　containing　the　sample　entropy　(SE)　of　variational　mode　decomposition　(VMD)　is　constructed　to　characterize　the　fault　information.　Secondly,　a　MSVM　model　containing　six　design　variables　is　established　and　then　optimized　by　GOA.　Finally,　the　optimized　MSVM　model　is　adopted　to　train　the　fault　feature　vectors　to　fulfill　fault　pattern　recognition.　In　order　to　verify　the　identification　accuracy　of　the　proposed　strategy,　two　sets　of　fault　signal　generated　from　a　rolling　bearing　test　rig　and　a　laboratory　planetary　gearbox　operating　under　different　load　conditions　are　analyzed.　The　diagnostic　results　manifest　that　the　proposed　strategy　can　fully　identify　different　level　faults　of　rolling　bearing　and　weak　faults　of　planetary　gearbox　as　well.　More　than　99%　identification　accuracy　of　the　proposed　strategy　is　further　highlighted　by　comparisons　with　other　five　SVM-based　methods.　The　present　method　provides　a　promising　solution　for　high　fault　identification　accuracy　in　rotating　machinery　working　under　different　loads.　©　2020　Elsevier　Ltd