Aiming　at　the　strong　background　noise　involved　in　the　signals　of　rolling　bearing　and　the　difficulty　to　extract　fault　feature　in　practice,　a　new　fault　diagnosis　method　is　proposed　based　on　Dual-tree　Complex　Wavelet　Transform　(DT-CWT)　and　AR　power　spectrum.　Firstly,　the　non-stationary　and　complex　vibration　signal　is　decomposed　into　several　different　frequency　band　components　through　dual-tree　complex　wavelet　decomposition;　Secondly,　Hilbert　envelope　is　formed　from　the　components　which　contains　the　fault　information.　Finally,　the　auto-power　spectrum　can　be　obtained　by　auto-regressive　(AR)　spectrum.　The　noise　interference　was　eliminated　effectively,　and　the　effective　signal　information　was　retained　at　the　same　time.　Thus,　the　fault　feature　information　was　extracted.　In　this　paper,　the　fault　test　and　the　engineering　practical　fault　data　of　rolling　bearing　were　analyzed　by　dual-tree　complex　wavelet　transform　and　AR　power　spectrum.　The　results　show　that　the　noise　of　the　vibration　signal　was　eliminated　effectively,　and　the　fault　feature　were　extracted.　The　feasibility　and　effectiveness　of　the　method　were　verified.　©　(2013)　Trans　Tech　Publications,　Switzerland.