电力系统设备状态监测的概念及现状

[1] Partial discharge plant -monitoring technology: present and future developments. Kemp I. IEE Proceedings Science Measurement and Technology . 1995

[2] Review of chemical indicators of degradation of cellulosic electrical paper insulation in oil -filled transformer. Emsley A,Stevens G. IEE Proceedings Science Measurement and Technology . 1994

[3] Artificial neural network based fault diagnostic of rotating machinery using wavelet transforms as a preprocessor. Paya B,et al. Journal of Mechanical Systems . 1997

[4] Feasibility of using unsupervised learning,artificial neural networks for the condition monitoring of electrical machines. Penman J,Yin C. IEE Proceddings on Electric Power Applications . 1994

[5] The determination of FFA concentration in transfomer oil by fluorescence measurements. Blue R,Uttamchandani D. IEEE Transactions on Dielectrics and Electrical Insulation . 1998

[6] Gas-turbine condition monitoring using qualitative model -based diagnosis. Trave-Massuyes L,Milne R. IEEE Expert . 1997

[7] Methods of condition monitoring and fault diagnosis for induction motors. Thorsen O,Dalva M. European Transactions on Telecommunications . 1998

[8] A study of the natural vibratory response of stator structures to improve condition monitoring strategies for induction motors. Lucas M,Thomson W. Proc of The Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science . 1998

[9] Wavelet analysis for gas turbine fault diagnostics. Aretakis N,Mathioudakis K. Transactions of ASME Journal of Engineering for Gas Turbines and Power . 1997

[10] Insulation failure prediction in AC machines using line-neural voltages. Cash M,et al. IEEE Transactions on Industry Applications . 1998

[1] Partial discharge plant -monitoring technology: present and future developments. Kemp I. IEE Proceedings Science Measurement and Technology . 1995

[2] Review of chemical indicators of degradation of cellulosic electrical paper insulation in oil -filled transformer. Emsley A,Stevens G. IEE Proceedings Science Measurement and Technology . 1994

[3] Artificial neural network based fault diagnostic of rotating machinery using wavelet transforms as a preprocessor. Paya B,et al. Journal of Mechanical Systems . 1997

[4] Feasibility of using unsupervised learning,artificial neural networks for the condition monitoring of electrical machines. Penman J,Yin C. IEE Proceddings on Electric Power Applications . 1994

[5] The determination of FFA concentration in transfomer oil by fluorescence measurements. Blue R,Uttamchandani D. IEEE Transactions on Dielectrics and Electrical Insulation . 1998

[6] Gas-turbine condition monitoring using qualitative model -based diagnosis. Trave-Massuyes L,Milne R. IEEE Expert . 1997

[7] Methods of condition monitoring and fault diagnosis for induction motors. Thorsen O,Dalva M. European Transactions on Telecommunications . 1998

[8] A study of the natural vibratory response of stator structures to improve condition monitoring strategies for induction motors. Lucas M,Thomson W. Proc of The Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science . 1998

[9] Wavelet analysis for gas turbine fault diagnostics. Aretakis N,Mathioudakis K. Transactions of ASME Journal of Engineering for Gas Turbines and Power . 1997

[10] Insulation failure prediction in AC machines using line-neural voltages. Cash M,et al. IEEE Transactions on Industry Applications . 1998