This　paper　proposed　a　signal　processing　method　based　on　principal　component　analysis　(PCA)　and　wavelet　analysis,　aiming　to　reduce　the　dimension　of　the　data　and　obtain　both　frequency　and　time　localization　information　which　could　help　to　find　abnormal　phenomenon　quickly　and　orient　the　position　and　the　time　of　faults　exactly　in　the　complex　textile　machinery　system.　At　first,　the　original　signals　were　simplified　by　principal　component　transform,　which　was　conducted　by　calculating　the　eigenvalue　and　eigenvector　of　correlation　coefficient　matrix,　and　by　defining　the　first　few　PCs　containing　most　of　the　variables　according　to　contribute　rate　and　cumulative　contribute　rate.　Secondly,　the　restructured　signals　were　decomposed　into　approximative　and　detailed　ones　for　obtaining　meaningful　captures　of　instantaneous　frequency　by　wavelet　analysis.　In　this　stage,　Hilbert　Envelope　Analysis　was　also　carried　out　to　the　first　layer　detail　signal　and　to　find　its　power　spectrum.　From　practical　application,　this　signal　processing　method　was　approved　validated.　©　2010　IEEE.