There　are　differences　among　different　levels　of　the　same　type　of　the　fault,　which　may　cause　misdiagnose.　A　fault　diagnosis　strategy　based　on　multi-scale　principal　component　analysis　and　kernel　entropy　component　analysis　(MSPCA-KECA)　is　proposed.　Taking　the　features　extracted　by　MSPCA　as　the　input　of　KECA　classifier　can　be　used　for　fault　online　detection　as　well　as　automatic　identification.　MSPCA　combines　wavelet　multi-scale　analysis　with　principal　component　analysis　to　select　the　scales　which　contain　fault-related　information,　and　then　use　PCA　to　extract　the　fault-related　features,　extracting　the　similarity　among　different　levels　of　the　same　type　of　fault　and　the　difference　among　different　faults,　which　can　improve　the　ability　of　fault　diagnosis.　The　combination　of　KECA　and　Cauchy-Schwarz　(CS)　statistics　extract　and　express　the　angular　structure　of　different　kinds　of　faults,　which　is　good　for　fault　classification.　The　control　limit　here　is　achieved　by　support　vector　data　description　(SVDD)　for　the　unacquainted　distribution　of　the　statistics.　Through　the　simulation　of　ASHRAR　1043-RP　chiller　data,　the　feasibility　and　effectiveness　of　the　MSPCA-KECA　method　are　verified.　©　All　Right　Reserved.