In　general,　vibration　signals　generated　by　the　switching　operation　of　a　high-voltage　circuit　breaker　(HVCB)　contains　important　information　to　reflect　its　mechanical　status.　A　method　for　mechanical　fault　diagnoses　of　an　HVCB　based　on　a　semisupervised　stacked　autoencoder　(SSAE)　and　an　integrated　extreme　learning　machine　(IELM)　is　proposed　in　this　study.　First,　the　complete　ensemble　empirical　mode　decomposition　with　adaptive　noise　(CEEMDAN)　is　used　to　decompose　the　vibration　signal　to　obtain　the　time-frequency　energy　matrix.　Then,　an　SSAE　model　is　applied　to　automatically　extract　the　characteristic　information　from　the　energy　matrix.　As　a　result,　two-level　classifiers　can　be　constructed.　The　first　level　is　utilized　to　identify　normal　or　abnormal　states,　and　the　second　level　is　selected　to　identify　various　types　of　faults　in　the　abnormal　state.　The　classifiers　of　these　levels　are　composed　of　binary　IELM.　The　advantages　of　the　proposed　method　are　that　it　not　only　can　automatically　extract　the　high-recognition　features　from　the　time-frequency　energy　matrix　of　high　dimension　to　complete　the　identification　of　the　existing　fault　types　in　the　training　set　but　also　can　accurately　identify　the　samples　of　unknown　types　of　faults.　Experimental　results　show　that　the　proposed　method　can　effectively　diagnose　mechanical　faults　of　an　HVCB,　and　the　classification　accuracy　reaches　99.5%.