Intelligent　fault　diagnosis　of　rotating　machinery　is　vital　for　industries　to　improve　fault　prediction　performance　and　reduce　the　maintenance　cost.　The　new　fault　diagnostic　framework　is　proposed　which　consists　of　three　stages:　(1)　signal　processing　and　feature　extraction,　(2)　fault　diagnosis　by　combining　the　classification　results　through　a　probabilistic　ensemble　method,　and　(3)　parameter　optimization　and　performance　evaluation.　In　the　first　stage,　ensemble　empirical　mode　decomposition　(EEMD)　decomposes　the　acquired　signal　into　a　suite　of　intrinsic　mode　functions　(IMF)　which　encounters　redundant　components　and　large　data　problems.　To　eliminate　the　redundant　IMF　and　select　fault　feature　from　residual　IMF,　correlation　coefficient　(CC)　and　singular　value　decomposition　(SVD)　method　are　applied　respectively.　In　the　second　stage,　to　improve　the　performance　of　fault　diagnosis　based　on　single　classifier　and　increase　the　number　of　detectable　fault,　a　new　probabilistic　committee　machine　(PCM)　method　is　proposed,　in　which　multiple　pairwise-coupled　sparse　Bayesian　extreme　learning　machines　(PCSBELM)　are　individually　trained　using　air　ration,　ignition　pattern　and　engine　sound　signal.　In　addition,　each　classifier　is　assigned　with　an　optimal　weight　in　accordance　with　their　reliability　and　accuracy　so　that　a　reliable　and　widely-covered　fault　diagnostic　result　can　be　obtained　from　the　weighted　combination　of　the　members.　To　verify　the　effectiveness　of　the　proposed　fault　diagnostic　framework,　it　is　applied　to　automotive　engine　fault　detection.　The　evaluation　results　show　the　proposed　framework　is　superior　to　the　existing　single　classifier　in　terms　of　both　single-　and　simultaneous-faults　in　automotive　engine.　©　2018　Elsevier　Ltd