As　key　electrical　equipment　in　the　power　system,　the　normal　operation　of　a　high-voltage　circuit　breaker　is　related　to　the　reliability　and　economy　of　the　power　supply.　In　this　paper,　a　mechanical　fault　diagnostic　method　for　a　high-voltage　circuit　breaker　via　the　hybrid　feature　extraction　and　the　integrated　extreme　learning　machine　(IELM)　is　investigated.　First,　the　complete　ensemble　empirical　mode　decomposition　with　adaptive　noise　(CEEMDAN)　is　used　to　decompose　the　vibration　signal　to　obtain　intrinsic　mode　functions　(IMF).　Then,　the　sub-band　reconstruction　of　each　order　IMF　component　is　performed　by　combining　the　Hilbert　transform　and　the　band-pass　filter　in　order　to　obtain　the　time-frequency　matrix.　Moreover,　mechanical　fault　feature　vectors　can　be　formed　by　the　time-frequency　entropy　and　the　singular　entropy,　which　are　extracted　by　transforming　the　time-frequency　matrix　into　the　energy　matrix　and　normalizing　the　frequency　bands　via　the　normal　cumulative　distribution　function　(NCDF).　In　addition,　an　IELM　is　built　for　the　fault　classification.　The　advantages　of　the　proposed　CEEMDAN　scheme　in　combination　with　band-pass　filtering　can　eliminate　the　modal　aliasing,　reduce　the　number　of　auxiliary　noise　additions,　and　improve　the　decomposition　efficiency.　Besides,　the　performance　of　the　singular　entropy　normalized　by　the　NCDF　is　more　stable,　and　the　IELM　composed　of　multiple　weak　classifiers　can　solve　the　shortcomings　of　the　traditional　extreme　learning　machine.　The　experimental　results　based　on　measured　data　show　the　proposed　method　can　effectively　diagnose　the　mechanical　failure　via　small　samples　of　high-voltage　circuit　breakers.　©　2013　IEEE.