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应用盲源分离中的JADE算法解决噪声的消除、弱信号的特征提取和多故障源的分离等问题。对大型风力机主轴振动信号进行盲源分离得到准确的故障信号,并根据故障振动信号频谱诊断出故障。利用盲源分离技术可以有效地去除外来干扰,以提高故障诊断精度,解决实际中故障定位、早期故障诊断率低的难题。诊断结果表明,该方法可有效地分离振动信号,并通过信号处理结果的频率特征来识别轴承故障状态。从而清晰地看出主轴的振动特征频率,实现对风力机主轴的故障诊断。
The JADE algorithm in blind source separation is applied to eliminate the noise, the feature extraction of weak signals and the separation of multiple fault sources. Blind source separation of the vibration signals of the large-scale wind turbine main shaft can obtain the accurate fault signal and diagnose the fault according to the spectrum of the fault vibration signal. The use of blind source separation technology can effectively remove external interference in order to improve the accuracy of fault diagnosis to solve the actual fault location, the early diagnosis of low fault rate problems. The diagnostic results show that this method can effectively separate the vibration signal and identify the bearing fault status through the frequency characteristics of the signal processing results. The characteristic frequency of the vibration of the main shaft can be clearly seen and the fault diagnosis of the main shaft of the wind turbine can be realized.