周期稀疏导向超小波在风力发电设备发电机轴承故障诊断中的应用

机械故障特征提取的内积变换原理要求匹配基函数与目标特征之间的相似性。在缺乏故障特征的精确信息这一不利条件下,根据故障呈现出的确定性以及统计特性能够有效指导基函数的选择和构造针对发电机轴承发生故障时常伴随周期性特征的先验知识,提出冲击故障特征周期性稀疏为导向的超小波构造方法。所提出的超小波变换利用可调品质因数小波变换作为匹配字典库,从而改进经典的基于单一固定基函数的小波分析思想。在技术路线上:首先采用超小波字典库对信号进行分解,计算各小波尺度上的周期性稀疏故障特征能量权重指标;以该权重指标优化为目标函数作为评价超小波字典与微弱故障特征匹配相适度的依据选择的可调品质因数小波最优刻画参数(即最优超小波);利用最优的超小波基函数对信号进行最终分解,获取其中的关键故障特征。所提出方法成功地应用于某风力发电机组上发电机轴承故障诊断,从中提取振动信号中隐藏的微弱冲击性故障特征。

Periodic Sparsity Oriented Super-wavelet Analysis with Application to Motor Bearing Fault Detection of Wind Turbine

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.