In　order　to　solve　the　problems　of　unsatisfactory　diagnosis　performance　and　unstable　model　of　conventional　fault　diagnosis　methods　for　transformers,　a　new　approach　based　on　improved　empirical　wavelet　transform　(IEWT)　and　salp　swarm　algorithm　(SSA)　optimized　kernel　extreme　learning　machine　(KELM)　is　proposed　in　this　study.　Firstly,　IEWT　is　used　to　adaptively　decompose　the　vibration　signal　to　obtain　a　set　of　empirical　wavelet　functions　(EWFs).　Secondly,　the　first　n-order　components　with　high　correlation　coefficient　are　collected.　Thirdly,　the　mean　value,　variance,　kurtosis,　refine　composite　multiscale　entropy　(RCMSE),　and　time-frequency　entropy(TFE)　of　these　n-order　components　are　calculated　to　construct　a　fusion　feature　vector.　Finally,　a　two-level　diagnostic　model　based　on　SSA-KELM　is　established.　The　first-level　of　it　is　applied　to　identify　normal　and　abnormal　states,　and　the　second-level　is　selected　to　identify　fault　categories　in　the　abnormal　states.　The　proposed　method　can　effectively　diagnose　the　existing　fault　categories　in　the　training　set　and　accurately　identify　the　unknown　categories　of　faults.　Experimental　results　show　that　the　proposed　method　can　efficiently　extract　features　of　different　vibration　signals　and　identify　the　faults,　with　an　average　classification　accuracy　of　96.25%.　It　is　better　than　other　methods,　such　as　wavelet　packet　energy　spectrum　analysis-KELM　and　EWT-fisher.　©　2021　Elsevier　Ltd