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。
贺王鹏
,
訾艳阳
,
陈彬强
,
姚斌
,
张周锁
. 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[J]. 机械工程学报, 2016
, 52(3)
: 41
-48
.
DOI: 10.3901/JME.2016.03.041
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.
[1] 陈雪峰，李继猛，程航，等. 风力发电机状态监测和故障诊断技术的研究与进展[J]. 机械工程学报，2011， 47(9)：45-52. CHEN Xuefeng，LI Jimeng，CHEN Hang，et al. Research and application of condition monitoring and fault diagnosis technology in wind turbines[J]. Journal of Mechanical Engineering， 2011， 47(9)：45-52.
[2] FENG Z P，ZUO M J，CHU F L，et al. Gear damage assessment based on cyclic spectral analysis[J]. IEEE Transactions on Reliability，2011，60(1)：21-32.
[3] LEI Y G，LIN J，ZUO M J，et al. Condition monitoring and fault diagnosis of planetary gearboxes： A review [J]. Measurement，2014，48(2)：292-305.
[4] 何正嘉，訾艳阳，张西宁. 现代信号处理及工程实例[M]．西安：西安交通大学出版社， 2007. HE Zhengjia，ZI Yanyang，ZHANG Xining. Modern signal processing and engineering application[M]. Xi’an： Xi’an Jiaotong University Press，2007.
[5] 袁静. 机械故障诊断的内积变换原理与多小波特征提取方法研究[D]. 西安：西安交通大学，2011. YUAN Jing. Inner product principle of mechanical fault diagnosis and feature extraction based on multiwavelet transforms [D]. Xi’an：Xi’an Jiaotong University， 2011.
[6] YAN R Q，GAO R X. Base wavelet selection for bearing vibration signal analysis[J]. International Journal of Wavelets，Multi-resolution，and Information Processing， 2009，7(4)：411-426.
[7] WANG S B，HUANG W G，ZHU Z K. Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis[J]. Mechanical Systems and Signal Processing，2011，25(4)：1299-1320.
[8] LI J M，CHEN X F，HE Z J，et al. A new noise-controlled second-order enhanced stochastic resonance method with its application in wind turbine drivetrain fault diagnosis[J]. Renewable Energy，2013，60：7-19.
[9] 严如强，钱宇宁，高晓旸，等. 基于小波域平稳子空间分析的风力发电机齿轮箱故障诊断[J]. 机械工程学报，2014，50(11)：9-16. YAN Ruqiang，QIAN Yuning，GAO Xiaoyang，et al. Wind turbine gearbox fault diagnosis based on wavelet domain stationary subspace analysis [J]. Journal of Mechanical Engineering，2014，50(11)：9-16.
[10] WANG D，SHEN C Q，TSE P W. A novel adaptive wavelet stripping algorithm for extracting the transients caused by bearing localized faults[J]. Journal of Sound and Vibration，2013，332(25)，6871-6890.
[11] WANG Y X，HE Z J，ZI Y Y. Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform[J]. Mechanical Systems and Signal Processing，2010，24(1)：119-137.
[12] 陈彬强. 过完备小波紧框架展开及其在机械设备故障监测诊断的应用[D]. 西安：西安交通大学，2013. CHEN Binqiang. Overcomplete frame expansion based on wavelet tight frame and its applications to machinery fault detection and diagnosis[D]. Xi’an：Xi’an Jiaotong University，2013.
[13] CHEN B Q，ZHANG Z S，ZI Y Y，et al. Detecting of transient vibration signatures using an improved fast spatial-spectral ensemble kurtosis kurtogram and its applications to mechanical signature analysis of short duration data from rotating machinery[J]. Mechanical Systems and Signal Processing，2013，40(1)：1-37.
[14] SELESNICK I W. Wavelet transform with tunable Q-factor[J]. IEEE Transactions on Signal Process，2011， 59(8)：3560-3575.
[15] CHEN B Q，ZHANG Z S，SUN C，et al. Fault feature extraction of gearbox by using overcomplete rational dilation discrete wavelet transform on signals measured from vibration sensors[J]. Mechanical Systems and Signal Processing，2012，33：275-298.
[16] HE W P，ZI Y Y，CHEN B Q，et al. Tunable Q-factor wavelet transform denoising with neighboring coefficients and its application to rotating machinery fault diagnosis[J]. Science China Technological Sciences，2013，56(8)：1956-1965.
[17] HE W P，ZI Y Y，CHEN B Q，et al. Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform [J]. Mechanical Systems and Signal Processing，2015，54：457-480.
[18] WANG H，CHEN J，DONG G. Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform[J]. Mechanical Systems and Signal Processing，2014.
[19] ANTONI J. Fast computation of the kurtogram for the detection of transient faults[J]. Mechanical Systems and Signal Processing，2007，21(1)：108-124.
