针对声发射检测齿轮箱轴承故障问题,提出基于奇异值分解(Singular Value Decomposition,SVD)与Fast Kurtogram算法的故障诊断方法。通过奇异值分解提高信号信噪比;将Fast Kurtogram算法用于故障信号共振解调带通滤波器参数确定,结合能...针对声发射检测齿轮箱轴承故障问题,提出基于奇异值分解(Singular Value Decomposition,SVD)与Fast Kurtogram算法的故障诊断方法。通过奇异值分解提高信号信噪比;将Fast Kurtogram算法用于故障信号共振解调带通滤波器参数确定,结合能量算子解调包络谱,成功提取齿轮箱轴承内外圈故障特征,有效改善传统共振解调中人工选择滤波器参数的不确定性。通过仿真与实验数据验证所提方法的有效性。展开更多
针对滚动轴承早期故障特征提取困难的问题,提出一种LMS(Least Mean Square,LMS)算法降噪、FastKurtogram选频和共振解调技术相结合的滚动轴承故障诊断方法。首先对采集到的信号进行自适应降噪,减弱背景噪声的影响;然后利用谱峭度值对故...针对滚动轴承早期故障特征提取困难的问题,提出一种LMS(Least Mean Square,LMS)算法降噪、FastKurtogram选频和共振解调技术相结合的滚动轴承故障诊断方法。首先对采集到的信号进行自适应降噪,减弱背景噪声的影响;然后利用谱峭度值对故障信号中瞬态成分敏感的特性,通过计算降噪后信号的快速峭度图,确定滤波器最优频带中心和带宽;最后进行共振包络解调提取出滚动轴承早期故障特征。通过仿真和实验验证分析,验证了该方法在滚动轴承早期故障诊断中的适用性和有效性。展开更多
This paper introduces an algorithm based on wavelet packet supported fast kurtogram and decision rules for the identification and classification of complex power quality(PQ)disturbances.Features are extracted from the...This paper introduces an algorithm based on wavelet packet supported fast kurtogram and decision rules for the identification and classification of complex power quality(PQ)disturbances.Features are extracted from the signals using fast kurtogram,envelope of filtered voltage signal and amplitude spectrum of squared envelop.Proposed algorithm can be implemented for the recognition of the complex PQ disturbances,which include the combination of voltage sag and harmonics,voltage momentary interruption(MI)and oscillatory transient(OT),voltage MI and harmonics,voltage sag and impulsive transient(IT),voltage sag,OT,IT and harmonics.Proposed work has been performed using the MATLAB software.Performance of the algorithm is compared with performance of algorithm supported by discrete wavelet transform(DWT)and fuzzy C-means clustering(FCM).展开更多
快速谱峭度(Fast Kurtogram,FK)通过构造有限冲击响应滤波器从频谱上将信号二分或三分为几个不同频带的分量后,判断每个分量的谱峭度大小以提取调制信息。该方法运算速度很快,但有时包含故障信息的频段无法被均分的谱峭度图容纳,甚至可...快速谱峭度(Fast Kurtogram,FK)通过构造有限冲击响应滤波器从频谱上将信号二分或三分为几个不同频带的分量后,判断每个分量的谱峭度大小以提取调制信息。该方法运算速度很快,但有时包含故障信息的频段无法被均分的谱峭度图容纳,甚至可能导致提取出的分量中无法检测到明显的故障信息。提出一种新的频谱边界划分方法用以优化快速谱峭度,并称之为经验快速谱峭度(Empirical Fast Kurtogram,EFK)。首先,将信号频谱的傅里叶变换函数中代表频谱趋势的成分提取出来,并搜索其极小值点序列;然后,以极小值点在频谱中的位置作为频谱划分的边界,采用Meyer小波构造滤波器并重构信号分量以求取峭度;最终,构造出一种新的快速谱峭度图,选择谱峭度最大的频段提取故障信息。该方法依据信号频谱的趋势划分边界可以有效地避免由于均分频谱导致的不合理现象,模拟信号及滚动轴承内圈、外圈故障信号证明了该方法的有效性。展开更多
文摘针对声发射检测齿轮箱轴承故障问题,提出基于奇异值分解(Singular Value Decomposition,SVD)与Fast Kurtogram算法的故障诊断方法。通过奇异值分解提高信号信噪比;将Fast Kurtogram算法用于故障信号共振解调带通滤波器参数确定,结合能量算子解调包络谱,成功提取齿轮箱轴承内外圈故障特征,有效改善传统共振解调中人工选择滤波器参数的不确定性。通过仿真与实验数据验证所提方法的有效性。
文摘针对滚动轴承早期故障特征提取困难的问题,提出一种LMS(Least Mean Square,LMS)算法降噪、FastKurtogram选频和共振解调技术相结合的滚动轴承故障诊断方法。首先对采集到的信号进行自适应降噪,减弱背景噪声的影响;然后利用谱峭度值对故障信号中瞬态成分敏感的特性,通过计算降噪后信号的快速峭度图,确定滤波器最优频带中心和带宽;最后进行共振包络解调提取出滚动轴承早期故障特征。通过仿真和实验验证分析,验证了该方法在滚动轴承早期故障诊断中的适用性和有效性。
文摘This paper introduces an algorithm based on wavelet packet supported fast kurtogram and decision rules for the identification and classification of complex power quality(PQ)disturbances.Features are extracted from the signals using fast kurtogram,envelope of filtered voltage signal and amplitude spectrum of squared envelop.Proposed algorithm can be implemented for the recognition of the complex PQ disturbances,which include the combination of voltage sag and harmonics,voltage momentary interruption(MI)and oscillatory transient(OT),voltage MI and harmonics,voltage sag and impulsive transient(IT),voltage sag,OT,IT and harmonics.Proposed work has been performed using the MATLAB software.Performance of the algorithm is compared with performance of algorithm supported by discrete wavelet transform(DWT)and fuzzy C-means clustering(FCM).
文摘快速谱峭度(Fast Kurtogram,FK)通过构造有限冲击响应滤波器从频谱上将信号二分或三分为几个不同频带的分量后,判断每个分量的谱峭度大小以提取调制信息。该方法运算速度很快,但有时包含故障信息的频段无法被均分的谱峭度图容纳,甚至可能导致提取出的分量中无法检测到明显的故障信息。提出一种新的频谱边界划分方法用以优化快速谱峭度,并称之为经验快速谱峭度(Empirical Fast Kurtogram,EFK)。首先,将信号频谱的傅里叶变换函数中代表频谱趋势的成分提取出来,并搜索其极小值点序列;然后,以极小值点在频谱中的位置作为频谱划分的边界,采用Meyer小波构造滤波器并重构信号分量以求取峭度;最终,构造出一种新的快速谱峭度图,选择谱峭度最大的频段提取故障信息。该方法依据信号频谱的趋势划分边界可以有效地避免由于均分频谱导致的不合理现象,模拟信号及滚动轴承内圈、外圈故障信号证明了该方法的有效性。
