该文采用Hilbert-Huang变换(HHT)对时变阻尼自由振动系统以及常见的Duffing振动系统和Van der Pol振动系统进行参数识别。首先通过经验模态分解将振动信号分解为自由振动信号和强迫振动信号,通过经验包络法得到分解后信号的振幅包络线...该文采用Hilbert-Huang变换(HHT)对时变阻尼自由振动系统以及常见的Duffing振动系统和Van der Pol振动系统进行参数识别。首先通过经验模态分解将振动信号分解为自由振动信号和强迫振动信号,通过经验包络法得到分解后信号的振幅包络线和瞬时频率。进而使用瞬时振幅及瞬时频率通过最小二乘法估计得到振动方程的各项参数。与小波识别结果进行对比,数值算例表明Hilbert-Huang变换可以有效地识别时变阻尼自由振动以及Duffing振动系统和Van der Pol振动系统的时变参数并且有较高精度。展开更多
To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery usin...To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.展开更多
文摘该文采用Hilbert-Huang变换(HHT)对时变阻尼自由振动系统以及常见的Duffing振动系统和Van der Pol振动系统进行参数识别。首先通过经验模态分解将振动信号分解为自由振动信号和强迫振动信号,通过经验包络法得到分解后信号的振幅包络线和瞬时频率。进而使用瞬时振幅及瞬时频率通过最小二乘法估计得到振动方程的各项参数。与小波识别结果进行对比,数值算例表明Hilbert-Huang变换可以有效地识别时变阻尼自由振动以及Duffing振动系统和Van der Pol振动系统的时变参数并且有较高精度。
基金Sponsored by the National Natural Science Foundation of China(Grant No.50875056)
文摘To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.
