Fault feature extraction of gearbox by using overcomplete rational dilation discrete wavelet transform on signals measured from vibration sensors
Fault feature extraction of gearbox by using overcomplete rational dilation discrete wavelet transform on signals measured from vibration sensors
复制标题
对振动传感器测量信号进行过完备有理扩张离散小波变换提取齿轮箱故障特征
DOI：
10.1016/j.ymssp.2012.07.007
复制
发表时间：
2012-11-01
影响因子：
8.4
通讯作者：
He, Zhengjia
中科院分区：
文献类型：
--
作者：
Chen, Binqiang;Zhang, Zhousuo;He, Zhengjia
齿轮箱故障诊断对于防止灾难性事故的发生具有重要意义。通过传感器测量的齿轮箱振动信号是有用和可靠的，因为它们携带着与变速箱机械故障相关的关键信息。为了提取齿轮箱振动信号中包含的故障特征，需要有效的信号处理技术。与经典的二进小波变换(DWT)相比，过完全有理伸缩离散小波变换(ORDWT)具有更好的平移不变性、可调的时频分布和灵活的小波原子可调谐等特点。由于这些优点，ORDWT被认为是一种通用的工具，可以适用于分析不同类型的齿轮箱故障特征，特别是在分析信号的非平稳和暂态特征方面。针对齿轮箱故障诊断中面临的各种故障特征提取问题，提出了一种基于ORDWT的故障特征提取方法。在该方法中，使用ORDWT作为前处理分解工具，并将相应的后处理方法与ORDWT相结合来提取特定类型的故障特征。为了提取信号中的周期性脉冲，提出了一种脉冲匹配算法。该算法采用了不同时频分布和不同振荡性质的ORDWT基，并利用峰度理论提出了一种改进的信号冲激度量来选择与隐含的周期脉冲匹配的最优ORDWT基。针对解调问题，提出了一种基于离散小波变换和希尔伯特变换相结合的改进的瞬时时频谱。对于信号去噪应用，ORDWT通过邻域系数收缩策略和子带选择步骤来增强，以揭示隐藏的瞬时振动内容。将提出的故障特征提取技术应用于一系列工程应用中，处理结果表明，在离散小波变换和经验模式分解方法效果较差的情况下，基于或离散小波变换的特征提取技术能够成功识别早期故障特征。(C)2012爱思唯尔有限公司。保留所有权利。
Gearbox fault diagnosis is very important for preventing catastrophic accidents. Vibration signals of gearboxes measured by sensors are useful and dependable as they carry key information related to the mechanical faults in gearboxes. Effective signal processing techniques are in necessary demands to extract the fault features contained in the collected gearbox vibration signals. Overcomplete rational dilation discrete wavelet transform (ORDWT) enjoys attractive properties such as better shift-invariance, adjustable time-frequency distributions and flexible wavelet atoms of tunable oscillation in comparison with classical dyadic wavelet transform (DWT). Due to these advantages, ORDWT is presented as a versatile tool that can be adapted to analysis of gearbox fault features of different types, especially in analyzing the non-stationary and transient characteristics of the signals. Aiming to extract the various types of fault features confronted in gearbox fault diagnosis, a fault feature extraction technique based on ORDWT is proposed in this paper. In the routine of the proposed technique, ORDWT is used as the pre-processing decomposition tool, and a corresponding post-processing method is combined with ORDWT to extract the fault feature of a specific type. For extracting periodical impulses in the signal, an impulse matching algorithm is presented. In this algorithm, ORDWT bases of varied time-frequency distributions and varied oscillatory natures are adopted, moreover an improved signal impulsiveness measure derived from kurtosis is developed for choosing optimal ORDWT bases that perfectly match the hidden periodical impulses. For demodulation purpose, an improved instantaneous time-frequency spectrum (ITFS), based on the combination of ORDWT and Hilbert transform, is presented. For signal denoising applications, ORDWT is enhanced by neighboring coefficient shrinkage strategy as well as subband selection step to reveal the buried transient vibration contents. The proposed fault feature extraction technique is applied in a range of engineering applications, and the processing results demonstrate that the ORDWT-based feature extraction technique successfully identifies the incipient fault features in the cases where DWT and empirical mode decomposition method are less effective. (C) 2012 Elsevier Ltd. All rights reserved.