Accurate　and　efficient　rotating　machinery　fault　diagnosis　is　crucial　for　industries　to　guarantee　the　productivity　and　reduce　the　maintenance　cost.　This　paper　systematically　proposes　a　new　fault　diagnosis　approach　including　signal　processing　techniques　and　pattern　recognition　method.　In　order　to　reveal　more　useful　details　in　a　fault　residing　signal,　a　novel　automatic　signal　segmentation　method　named　Grassmann　manifold　–　angular　central　Gaussian　distribution　is　proposed　to　divide　a　raw　signal　into　several　segments,　resulting　in　a　significant　improvement　of　diagnosis　accuracy.　An　improved　empirical　mode　decomposition,　wavelet　transform　–　ensemble　empirical　mode　decomposition,　is　also　designed　which　could　adequately　solve　the　problems　of　mode　mixing　and　end　effects.　Moreover,　a　morphological　method　usually　used　in　image　processing　is　investigated　and　adopted　to　change　the　shape　of　the　intrinsic　mode　functions　to　further　reveal　the　faulty　impulses.　In　order　to　reduce　the　high　dimension　of　the　extracted　features　and　improve　the　computational　efficiency　and　accuracy,　a　deep　belief　network　is　designed　to　conduct　information　fusion,　and　compared　with　widely　adopted　kernel　principal　component　analysis.　For　classification,　a　pairwise　coupling　strategy　is　proposed　and　combined　with　sparse　Bayesian　extreme　learning　machine.　The　experiments　conducted　using　the　proposed　approach　demonstrate　the　effectiveness　of　the　proposed　system.　©　2018　Elsevier　Ltd