针对风电机组滚动轴承工作环境恶劣、工况多变且振动信号成分复杂等特点,将33项时域和频域特征参数及其特性应用于风电机组滚动轴承状态监测和故障诊断中,利用奇异值分解重构法(Singular Value Decomposition,SVD)将滚动轴承振动故障信...针对风电机组滚动轴承工作环境恶劣、工况多变且振动信号成分复杂等特点,将33项时域和频域特征参数及其特性应用于风电机组滚动轴承状态监测和故障诊断中,利用奇异值分解重构法(Singular Value Decomposition,SVD)将滚动轴承振动故障信号中的噪声等干扰成分去除,降噪重构后的信号经过基于经验模式分解法(Empirical Mode Decomposition,EMD)的希尔伯特-黄变换,实现故障冲击信号的共振解调处理,将低频周期故障调制信号筛选出来,最终结合滚动轴承各部件故障特征频率、振动信号时频分析结果和时频特征参数诊断结果实现滚动轴承的状态监测和故障识别。并通过振动测试信号分析,验证了该方法对提取风电机组滚动轴承故障特征的有效性。展开更多
In audio classification applications, features extracted from the frequency domain representation of signals are typically focused on the magnitude spectral content, while the phase spectral content is ignored. The co...In audio classification applications, features extracted from the frequency domain representation of signals are typically focused on the magnitude spectral content, while the phase spectral content is ignored. The conventional Fourier Phase Spectrum is a highly discontinuous function;thus, it is not appropriate for feature extraction for classification applications, where function continuity is required. In this work, the sources of phase spectral discontinuities are detected, categorized and compensated, resulting in a phase spectrum with significantly reduced discontinuities. The Hartley Phase Spectrum, introduced as an alternative to the conventional Fourier Phase Spectrum, encapsulates the phase content of the signal more efficiently compared with its Fourier counterpart because, among its other properties, it does not suffer from the phase ‘wrapping ambiguities’ introduced due to the inverse tangent function employed in the Fourier Phase Spectrum computation. In the proposed feature extraction method, statistical features extracted from the Hartley Phase Spectrum are combined with statistical features extracted from the magnitude related spectrum of the signals. The experimental results show that the classification score is higher in case the magnitude and the phase related features are combined, as compared with the case where only magnitude features are used.展开更多
文摘针对风电机组滚动轴承工作环境恶劣、工况多变且振动信号成分复杂等特点,将33项时域和频域特征参数及其特性应用于风电机组滚动轴承状态监测和故障诊断中,利用奇异值分解重构法(Singular Value Decomposition,SVD)将滚动轴承振动故障信号中的噪声等干扰成分去除,降噪重构后的信号经过基于经验模式分解法(Empirical Mode Decomposition,EMD)的希尔伯特-黄变换,实现故障冲击信号的共振解调处理,将低频周期故障调制信号筛选出来,最终结合滚动轴承各部件故障特征频率、振动信号时频分析结果和时频特征参数诊断结果实现滚动轴承的状态监测和故障识别。并通过振动测试信号分析,验证了该方法对提取风电机组滚动轴承故障特征的有效性。
文摘In audio classification applications, features extracted from the frequency domain representation of signals are typically focused on the magnitude spectral content, while the phase spectral content is ignored. The conventional Fourier Phase Spectrum is a highly discontinuous function;thus, it is not appropriate for feature extraction for classification applications, where function continuity is required. In this work, the sources of phase spectral discontinuities are detected, categorized and compensated, resulting in a phase spectrum with significantly reduced discontinuities. The Hartley Phase Spectrum, introduced as an alternative to the conventional Fourier Phase Spectrum, encapsulates the phase content of the signal more efficiently compared with its Fourier counterpart because, among its other properties, it does not suffer from the phase ‘wrapping ambiguities’ introduced due to the inverse tangent function employed in the Fourier Phase Spectrum computation. In the proposed feature extraction method, statistical features extracted from the Hartley Phase Spectrum are combined with statistical features extracted from the magnitude related spectrum of the signals. The experimental results show that the classification score is higher in case the magnitude and the phase related features are combined, as compared with the case where only magnitude features are used.
