Aiming at the non-linear nature of the signals generated from turbines, curvilinear component analysis (CCA), a novel nonlinear projection method that favors local topology conservation is presented for turbines condi...Aiming at the non-linear nature of the signals generated from turbines, curvilinear component analysis (CCA), a novel nonlinear projection method that favors local topology conservation is presented for turbines conditions monitoring. This is accomplished in two steps. Time domain features are extracted from raw vibration signals, and then they are projected into a two-dimensional output space by using CCA method and form regions indicative of specific conditions, which helps classify and identify turbine states visually. Therefore, the variation of turbine conditions can be observed clearly with the trajectory of image points for the feature data in the two-dimensional space, and the occurrence and development of failures can be monitored in time. Key words condition monitoring - turbines - nonlinear mapping - curvilinear component analysis CLC number TP 17 - TH 17 Foundation item: Supported by the National Key Basic Research Special Found of China (2003CB716207) and the National Natural Science Foundation of China (50375047)Biography: Liao Guang-lan (1974-), male, Ph. D. candidate, research direction: fault diagnosis, pattern recognition and neural networks.展开更多
文摘Aiming at the non-linear nature of the signals generated from turbines, curvilinear component analysis (CCA), a novel nonlinear projection method that favors local topology conservation is presented for turbines conditions monitoring. This is accomplished in two steps. Time domain features are extracted from raw vibration signals, and then they are projected into a two-dimensional output space by using CCA method and form regions indicative of specific conditions, which helps classify and identify turbine states visually. Therefore, the variation of turbine conditions can be observed clearly with the trajectory of image points for the feature data in the two-dimensional space, and the occurrence and development of failures can be monitored in time. Key words condition monitoring - turbines - nonlinear mapping - curvilinear component analysis CLC number TP 17 - TH 17 Foundation item: Supported by the National Key Basic Research Special Found of China (2003CB716207) and the National Natural Science Foundation of China (50375047)Biography: Liao Guang-lan (1974-), male, Ph. D. candidate, research direction: fault diagnosis, pattern recognition and neural networks.
