ABSTRACT　　The　invention　relates　to　a　fault　diagnosis　method　for　rolling　bearing　based　on　generalized　　composite　multi-scale　weighted　permutation　entropy　and　supervised　isometric　mapping,　　which　　comprises　the　following　steps　:　　The　rolling　bearing　signals　under　different　fault　states　are　　collected;　　The　generalized　composite　multi-scale　weighted　permutation　entropy　algorithm　　(GCMWPE)　is　used　to　extract　fault　features,　　and　the　high-dimensional　fault　feature　set　of　　rolling　bearings　is　constructed　comprehensively　from　multiple　scales;　　A　novel　manifold　　learning　algorithm,　　named　supervised　isometric　mapping　(S-Isomap),　　is　used　to　reduce　the　　dimensionality　of　high-dimensional　fault　features　and　obtain　a　low-dimensional　fault　feature　　set;　　The　low-dimensional　fault　feature　set　is　used　to　train　PSO-SVM,　　and　the　trained　particle　　swarm　optimization　support　vector　machine　PSO-SVM　is　used　for　diagnosing　bearing　faults.　　　The　method　solves　the　problem　of　difficulty　in　extracting　fault　features　of　rolling　bearings,　　and　　can　effectively　and　accurately　diagnose　various　fault　types　of　the　rolling　bearing.　1/5　　FIGURES　　Vib　rationacceleration　signal　　Trainingsample　Testsample　　Constructing　high-dimensional　fault　feature　set　usingGCMWPE　　Dimension　reduction　using　S-Isornap　manifold　leading　algorithm　　Low　W　itt　Collection　of　Training　Samples　Test　sample　low　W　itt　collection　　Train　PSO-SVM　classifier　Trained　PSO-SVM　classifier　model　　Diagnostic　fault　type　　Figure　I