Periodic Sparsity Oriented Super-wavelet Analysis with Application to Motor Bearing Fault Detection of Wind Turbine
Periodic Sparsity Oriented Super-wavelet Analysis with Application to Motor Bearing Fault Detection of Wind Turbine
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DOI：
10.3901/jme.2016.03.041
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发表时间：
2016
影响因子：
--
通讯作者：
Wangpeng He
中科院分区：
文献类型：
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作者：
Wangpeng He
： 内积变换原理要求匹配基函数与期望特征高度相似。然而，在没有潜在故障特征的精确信息的情况下，所研究特征的确定性或统计特征有利于选择和构建适当的匹配基。根据重复脉冲故障特征固有的周期性稀疏现象，提出了一种基于周期性稀疏性的定向超小波变换。超小波变换是在可调谐Q因子小波变换(TQWT)的基础上构建的，是对传统的唯一固定基思想的改进。程序中应用超小波字典函数对信号进行分解；采用估计周期性稀疏特征能量比（PSFER）的指标来指导TQWT参数的选择；利用所选的最优超小波基来揭示信号中隐藏的故障特征。该技术应用于获取风力发电设备电机轴承的早期故障特征，提取的特征被证明与实际轴承故障相关。
： The demand of high similarity between the matching basis function and expected feature is required by the inner product transform principle. However, without precise information of the potential fault features, the deterministic or statistical characteristics of the investigated features are beneficial to the selection and construction of proper matching bases. According to the intrinsic periodic sparsity phenomena of repetitive impulsive fault features, a periodic sparsity based oriented super-wavelet transform is proposed. The super-wavelet transform is constructed based on the tunable Q-factor wavelet transform (TQWT) and presented as an improvement to the conventional idea of unique and fixed basis. Within the procedure, the super-wavelet dictionary functions are applied to decompose signals; an indicator estimating the periodic sparsity feature energy ratio (PSFER) is adopted to guide the selection of TQWT’s parameters; the selected optimal super-wavelet basis is utilized to reveal the hidden fault features in the signal. The proposed technique is applied to acquire the incipient fault features of a motor bearing on a piece of wind power generation equipment, and the extracted features proved to be associated with an actual bearing fault.