A new method of fault diagnosis of in-situ sampled vibration signals is introduced for the non-stationery signals of rotating machines by using the harmonic wavelet filtering.
针对旋转机械非平稳信号的特点，提出采用谐波小波滤波对现场采集的振动信号进行故障诊断的新方法。
The central content of this thesis includes identifying and revising the critical rotate speed-dynamic parameters of rotor-bearing system by the artificial neural network theory and harmonic wavelet.
本论文的核心内容是：应用谐波小波和人工神经网络理论对转子轴承系统动力参数——临界转速进行识别与修正。
The central content of this thesis includes identifying and revising the critical rotate speed-dynamic parameters of rotor-bearing system by the artificial neural network theory and harmonic wavelet.
本论文的核心内容是：应用谐波小波和人工神经网络理论对转子轴承系统动力参数——临界转速进行识别与修正。
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