In the case of fault diagnosis for roller bearings, the conventional diagnosis approaches by using the time interval of energy impacts in time-frequency distribution or the pass-frequencies are based on the assumption...In the case of fault diagnosis for roller bearings, the conventional diagnosis approaches by using the time interval of energy impacts in time-frequency distribution or the pass-frequencies are based on the assumption that machinery operates under a constant rotational speed. However, when the rotational speed varies in the broader range, the pass-frequencies vary with the change of rotational speed and bearing faults cannot be identified by the interval of impacts. Researches related to automatic diagnosis for rotational machinery in variable operating conditions were quite few. A novel automatic feature extraction method is proposed based on a pseudo-Wigner-Ville distribution （PWVD） and an extraction of symptom parameter （SP）. An extraction method for instantaneous feature spectrum is presented using the relative crossing information （RCI） and sequential inference approach, by which the feature spectrum from time-frequency distribution can be automatically, sequentially extracted. The SPs are considered in the frequency domain using the extracted feature spectrum to identify among the conditions of a machine. A method to obtain the synthetic symptom parameter is also proposed by the least squares mapping （LSM） technique for increasing the diagnosis sensitivity of SP. Practical examples of diagnosis for bearings are given in order to verify the effectiveness of the proposed method. The verification results show that the features of bearing faults, such as the outer-race, inner-race and roller element defects have been effectively extracted, and the proposed method can be used for condition diagnosis of a machine under the variable rotational speed.展开更多
运用平滑伪Wigner-Ville分布提取大脑皮层的稳态视觉诱发电位(steady state visual evoked potential,SSVEP)信号,并作为输入信号应用于脑机接口系统。研究结果表明,该方法不需叠加平均信号就可以提高信噪比,提取出的稳态视觉诱发电位...运用平滑伪Wigner-Ville分布提取大脑皮层的稳态视觉诱发电位(steady state visual evoked potential,SSVEP)信号,并作为输入信号应用于脑机接口系统。研究结果表明,该方法不需叠加平均信号就可以提高信噪比,提取出的稳态视觉诱发电位信号能够准确反映使用者的控制意图,为脑机接口选取特征提供了依据。展开更多
基金supported by National Natural Science Foundation of China (Grant No. 50875016, 51075023)Fundamental Research Funds for the Central Universities of China (Grant No. JD0903, JD0904)
文摘In the case of fault diagnosis for roller bearings, the conventional diagnosis approaches by using the time interval of energy impacts in time-frequency distribution or the pass-frequencies are based on the assumption that machinery operates under a constant rotational speed. However, when the rotational speed varies in the broader range, the pass-frequencies vary with the change of rotational speed and bearing faults cannot be identified by the interval of impacts. Researches related to automatic diagnosis for rotational machinery in variable operating conditions were quite few. A novel automatic feature extraction method is proposed based on a pseudo-Wigner-Ville distribution （PWVD） and an extraction of symptom parameter （SP）. An extraction method for instantaneous feature spectrum is presented using the relative crossing information （RCI） and sequential inference approach, by which the feature spectrum from time-frequency distribution can be automatically, sequentially extracted. The SPs are considered in the frequency domain using the extracted feature spectrum to identify among the conditions of a machine. A method to obtain the synthetic symptom parameter is also proposed by the least squares mapping （LSM） technique for increasing the diagnosis sensitivity of SP. Practical examples of diagnosis for bearings are given in order to verify the effectiveness of the proposed method. The verification results show that the features of bearing faults, such as the outer-race, inner-race and roller element defects have been effectively extracted, and the proposed method can be used for condition diagnosis of a machine under the variable rotational speed.
文摘运用平滑伪Wigner-Ville分布提取大脑皮层的稳态视觉诱发电位(steady state visual evoked potential,SSVEP)信号,并作为输入信号应用于脑机接口系统。研究结果表明,该方法不需叠加平均信号就可以提高信噪比,提取出的稳态视觉诱发电位信号能够准确反映使用者的控制意图,为脑机接口选取特征提供了依据。
