Fractal Lifting Wavelets for Machine Fault Diagnosis
Fractal Lifting Wavelets for Machine Fault Diagnosis
复制标题
DOI：
10.1109/access.2019.2908213
复制
发表时间：
2019-03
期刊：
影响因子：
3.9
通讯作者：
Binqiang Chen;Y. Li;Nianyin Zeng;Wangpeng He
中科院分区：
文献类型：
--
作者：
Binqiang Chen;Y. Li;Nianyin Zeng;Wangpeng He
故障诊断对现代机电系统的安全可靠运行具有重要意义。先进的信号处理技术是必不可少的，从测量的动态信号中提取早期特征。对于离散小波分析，平移不变性和适当的频率-尺度配置都是必要的早期故障特征的有效调查。提出了一种基于冗余第二代小波包分解（RSGWPD）的分形提升算法。利用分形提升方法生成的隐式小波包可以实现一种新的集中式多分辨分析。证明了每个IWP继承了冗余提升格式的精确平移不变性。此外，一个新的概念，嵌套的集中小波包聚类被引入解释的优点，由IWP组成的集合。通过数值模拟，验证了精确移不变性在离散时间序列多尺度分析中的优势。将RSGWPD和IWP相结合，对振动测量进行多尺度扩展。为了进一步探索最佳的功能，空间光谱集成峰度的指标被用来选择最佳的分析参数。该方法已成功应用于齿轮箱故障诊断和轴承故障诊断的实例研究。将该方法的结果与其他主流自适应信号分解方法的结果进行了比较。实验结果表明，精确平移不变性和集中式多分辨率相结合，显著提高了非平稳振动信号早期故障特征提取的性能。
Fault diagnosis is of vital importance in safety and reliable operations of modern electromechanical systems. Advanced signal processing techniques are indispensable for extracting incipient features from measured dynamical signals. For discrete wavelet analysis, shift-invariance and proper frequency-scale configuration are both necessary for effective investigation of incipient fault features. In this paper, a novel fractal lifting scheme is proposed based on redundant second generation wavelet packet decomposition (RSGWPD). Implicit wavelet packets (IWPs), generated via fractal lifting scheme, can realize a novel centralized multiresolution. It is demonstrated that each IWP inherits the property of exact shift-invariance originated from redundant lifting scheme. In addition, a novel concept of nested centralized wavelet packet cluster is introduced for explaining merits provided by sets composed of IWPs. The numerical simulations were employed to validate the benefits of exact shift-invariance in a multiscale analysis of discrete time series. RSGWPD and IWPs are combined to conduct multiscale expansion of vibration measurement. To further explore optimal features, an indicator of spatial-spectral ensemble kurtosis is utilized to select optimal analysis parameters. The proposed technique was successfully applied to case studies of gearbox fault diagnosis as well as bearing fault diagnosis. The comparisons were made between results by the proposed technique and those provided by some other mainstream adaptive signal decomposition methodologies. It is verified that the combination of exact shift-invariance and centralized multiresolution significantly enhances the performance of incipient fault feature extraction of nonstationary vibration signals.