为准确提取被强背景噪声掩盖的滚动轴承故障信息,提出一种参数优化特征模态分解(parameter-optimized feature mode decomposition,POFMD)方法。首先,为解决特征模态分解(feature mode decomposition,FMD)方法的输入参数依赖人工经验选...为准确提取被强背景噪声掩盖的滚动轴承故障信息,提出一种参数优化特征模态分解(parameter-optimized feature mode decomposition,POFMD)方法。首先,为解决特征模态分解(feature mode decomposition,FMD)方法的输入参数依赖人工经验选取的问题,以平方包络谱峭度(kurtosis of the square envelope spectrum,KSES)为权值,结合平方包络谱基尼系数(Gini index of the square envelope spectrum,GISES)构建加权平方包络谱基尼系数(weighted Gini index of the square envelope spectrum,WGISES)作为目标函数,通过优化算法确定FMD的最优参数组合;其次,为解决FMD的主模态分量难以选取的问题,通过计算所分解模态分量的KSES值选取主模态分量;最后,通过包络谱分析实现故障诊断。经仿真信号和实测信号分析,验证了POFMD在强背景噪声下滚动轴承故障诊断中的有效性。与变分模态分解、最大相关峭度解卷积和谱峭度相比,POFMD有更优越的故障特征提取性能。展开更多
Planetary transmission plays a vital role in wind turbine drivetrains, and its fault diagnosis has been an important and challenging issue. Owing to the complicated and coupled vibration source, time-variant vibration...Planetary transmission plays a vital role in wind turbine drivetrains, and its fault diagnosis has been an important and challenging issue. Owing to the complicated and coupled vibration source, time-variant vibration transfer path, and heavy background noise masking effect, the vibration signal of planet gear in wind turbine gearboxes exhibits several unique characteristics： Complex frequency components, low signal-to-noise ratio, and weak fault feature. In this sense, the periodic impulsive components induced by a localized defect are hard to extract, and the fault detection of planet gear in wind turbines remains to be a challenging research work. Aiming to extract the fault feature of planet gear effectively, we propose a novel feature extraction method based on spectral kurtosis and time wavelet energy spectrum （SK-TWES） in the paper. Firstly, the spectral kurtosis （SK） and kurtogram of raw vibration signals are computed and exploited to select the optimal filtering parameter for the subsequent band-pass filtering. Then, the band-pass filtering is applied to extrude periodic transient impulses using the optimal frequency band in which the corresponding SK value is maximal. Finally, the time wavelet energy spectrum analysis is performed on the filtered signal, selecting Morlet wavelet as the mother wavelet which possesses a high similarity to the impulsive components. The experimental signals collected from the wind turbine gearbox test rig demonstrate that the proposed method is effective at the feature extraction and fault diagnosis for the planet gear with a localized defect.展开更多
文摘为准确提取被强背景噪声掩盖的滚动轴承故障信息,提出一种参数优化特征模态分解(parameter-optimized feature mode decomposition,POFMD)方法。首先,为解决特征模态分解(feature mode decomposition,FMD)方法的输入参数依赖人工经验选取的问题,以平方包络谱峭度(kurtosis of the square envelope spectrum,KSES)为权值,结合平方包络谱基尼系数(Gini index of the square envelope spectrum,GISES)构建加权平方包络谱基尼系数(weighted Gini index of the square envelope spectrum,WGISES)作为目标函数,通过优化算法确定FMD的最优参数组合;其次,为解决FMD的主模态分量难以选取的问题,通过计算所分解模态分量的KSES值选取主模态分量;最后,通过包络谱分析实现故障诊断。经仿真信号和实测信号分析,验证了POFMD在强背景噪声下滚动轴承故障诊断中的有效性。与变分模态分解、最大相关峭度解卷积和谱峭度相比,POFMD有更优越的故障特征提取性能。
基金The authors gratefully appreciate all the reviewers and the editor for their valuable comments and advices about our manuscript. The authors gratefully acknowledge tile support of this research work by the National Natural Science Foundation of China （Grant No. 51335006）.
文摘Planetary transmission plays a vital role in wind turbine drivetrains, and its fault diagnosis has been an important and challenging issue. Owing to the complicated and coupled vibration source, time-variant vibration transfer path, and heavy background noise masking effect, the vibration signal of planet gear in wind turbine gearboxes exhibits several unique characteristics： Complex frequency components, low signal-to-noise ratio, and weak fault feature. In this sense, the periodic impulsive components induced by a localized defect are hard to extract, and the fault detection of planet gear in wind turbines remains to be a challenging research work. Aiming to extract the fault feature of planet gear effectively, we propose a novel feature extraction method based on spectral kurtosis and time wavelet energy spectrum （SK-TWES） in the paper. Firstly, the spectral kurtosis （SK） and kurtogram of raw vibration signals are computed and exploited to select the optimal filtering parameter for the subsequent band-pass filtering. Then, the band-pass filtering is applied to extrude periodic transient impulses using the optimal frequency band in which the corresponding SK value is maximal. Finally, the time wavelet energy spectrum analysis is performed on the filtered signal, selecting Morlet wavelet as the mother wavelet which possesses a high similarity to the impulsive components. The experimental signals collected from the wind turbine gearbox test rig demonstrate that the proposed method is effective at the feature extraction and fault diagnosis for the planet gear with a localized defect.
