摘要
当旋转机械发生故障时,振动信号通常表现为非平稳性,常规的非平稳信号分析方法虽然在一定程度上解决了非平稳信号的故障特征提取问题,但由于诊断对象为单源信息,分析结果存在信号信噪比低和诊断的可信度低等问题。为了从振动信号中更全面、准确地提取出故障征兆,本文将全矢谱技术引入到非线性、非平稳信号分析方法中,实现多通道融合信息的小波变换、高阶统计量以及经验模态分解等非平稳信号分析方法,解决多通道非平稳信号的数据融合与特征提取等问题。
本文在“全矢谱技术体系构建及故障诊断基础研究”(国家自然科学基金项目(50675209))和“全矢谱技术及设备诊断工程应用研究”(河南省杰出人才创新基金(0621000500))项目的基础上,研究了旋转机械多个通道的故障振动信号的非平稳信号分析方法。主要研究内容包括四部分：一是基于全矢谱技术的小波分析方法及其在故障诊断中的应用；二是基于全矢谱技术的高阶统计量分析方法及其在故障诊断中的应用：三是基于全矢谱技术的经验模态分解及其在故障诊断中的应用；四是全矢谱技术体系的构建及其应用。各个部分的具体研究内容和主要成果如下：
(1)研究了基于信息融合的全矢小波、全矢小波包在旋转机械故障诊断中的应用。针对故障振动信号的非平稳特性,将全矢小波包分析方法与基于Hilbert变换的包络解调相结合,提出基于同源信息融合的全矢小波包-包络解调分析方法,并在仿真和实例分析中进行验证,即分别采用传统的单源小波包-包络解调、全矢Hilbert包络解调和全矢小波包-包络解调三种方法对同源信息进行分析,通过对比分析表明,全矢小波包-包络解调有效地融合了来自不同方向传感器的振动信息,能够全面、准确地提取出特征频率,具有一定的优越性。在仿真实验分析中,对全矢小波包-包络解调分析方法的兼容性作了进一步讨论。
(2)研究了基于同源信息融合的矢双谱理论,对同一截面的单源信号分别作双谱分析时,两者在能量和结构上存在较大差别,矢双谱分析方法通过对同源信息的有效融合,能够完整地表达出振动信号中的二次相位耦合特征,具有一定的优越性。针对工程中的大量背景噪声,进一步提出小波包-矢双谱,该方法可以提高信噪比,比直接进行矢双谱分析的效果更好。
(3)研究了Wigner三谱在非平稳故障振动信号中的应用。对转子同一截面不同方向传感器的振动信息分别进行Wigner三谱分析时,由于转子的涡动特性,其结果呈现出差异性,将全矢谱与Wigner三谱相结合,提出矢Wigner三谱的理论及算法,并通过实例验证了该方法的可行性。
(4)研究了基于同源信息融合的全矢EMD分析方法,仿真和实例分析表明,全矢EMD能够将转子同一截面不同方向处的信号有效融合,提取出该截面最大振动强度的EMD结果,减少了EMD分解由于振动幅值的不同带来的分析误差：对多分量叠加信号进行EMD分解时,由于受信号频率相对大小的影响,EMD分解结果产生频率混叠现象,全矢EEMD分析方法能够有效地抑制模态混叠,更真实、准确地反映出各个模态分量的物理特性；针对分解后具有调制特性的模态分量,进一步对分解结果作包络解调分析,分别给出了全矢EMD包络解调和全矢EEMD包络解调两种分析方法。
(5)在以上理论研究的基础上,构建基于全矢谱技术的非平稳故障诊断体系,探讨了以全矢谱技术为核心的振动信号分析系统的总体设计过程,并给出了系统的主要功能和技术特点,为进一步拓展全矢谱技术的工程应用范围奠定了基础。
When rotating machinery failure, vibration signals showed non-stationary characteristics. Although traditional non-stationary signal analysis method could extract fault feature, the results were incomplete and imprecise because of single data. In order to overcome the limitations due to incomplete and imprecise information, two mutually orthogonal sensors were usually installed on the same section of a rotor in the rotating machine field. The dissertation introduced full vector spectrum technology based on information fusion. Some new basic algorithms of analysis methods were proposed, which involved full vector wavelet analysis, full vector higher-order spectrum analysis and full vector empirical mode decomposition analysis. The methods solved the data fusion of multi-channel nonstable signal, and extracted the fault feature.
The research based on the prejects of "Basic research on full vector spectrum technology system construction and fault diagnosis"(National Natural Science Foundation of China, Grant No.50675209), and the "The engineering application research on full vector spectrum and fault diagnosis "(the Outstanding Talent Innovation Foundation of Henan, Grant No.0621000500). The theories and methods of non-stationary signal which are from two different sensors were studied. The primary researches of the dissertation include four parts:The first part is wavelet analysis method based on the full vector spectrum technology and its application in fault diagnosis; the second part is higher order statistics method based on the full vector spectrum and its application in fault diagnosis; the third part is empirical mode decomposition based on full vector spectrum and its application in fault diagnosis; the fourth part is to construct full vector spectrum technology system. The main achievements and precise contents of the above three primary parts are summarized as the follows.
(1) Research on full vector wavelet transform and full vector wavelet packet and its application in the fault diagnosis of rotating machines. Aiming at the non-stationary vibrating characteristics of rotating machine, a new method of full vector wavelet packet-envelope was presented by merging vibration signal from two mutually orthogonal sensors. Compared to the new method, the limits of the traditional wavelet packet-envelope and full vector Hilbert envelope analysis method were demonstrated by showing their application to simulated data and actual signals. Data fusion of vibration signals was considered as evidences for the validity of the new technique. Results show that the new approach is more effective. Furthermore, this part discussed the compatibility of the new method.
(2) Considering multi-source information on the same section of a rotor, research on vector-bispectrum analysis method. The drawback of using general bispectrum taking individual source information alone is integral to display the nonlinear properties. The analysis conclusions of vector-bispectrum are more complete and reliable. In view of a large number of background noise from engineering equipment, a new method for combining wavelet packet with vector-bispectrum was put forward. Compared to the vector-bispectrum analysis, the proposed method can improve the signal-to-noise ratio.
(3) The Wigner trispectrum was studied in the application of non-stationary fault vibration signals. The Wigner trispectrum analysis method based on one single source could cause some fuzzy results because of ignoring information of the relationship between the two sensors. Vector Wigner trispectrum was proposed by combining full vector spectrum technology with Wigner trispectrum, and applied to rotating machine fault diagnosis. Simulation and experimental data show that the proposed method provided a more reliable and complete results.
(4) Research on full vector empirical mode decomposition analysis method based on full vector spectrum. To deal with the error caused by different vibrating amplitude, merged data was analyzed by performing full vector EMD method. It fused the double channels information and inherited the superiority of the traditional EMD. For superposition of multi-component signal, the results of full vector empirical mode decomposition were affected by the relative size of signal frequencies. Full vector ensemble empirical mode decomposition based on data fusion was raised to solve the issue of mixed mode in the empirical mode decomposition analysis method. The characteristic frequencies could be clearly obtained. Both simulated data and actual data collected from engineering equipment are used to verify the effectiveness of the proposed method. According to the modulating characteristics of each intrinsic mode function, two analysis methods of full vector EMD demodulation and full vector EEMD demodulation were given.
(5) Based on the above theoretical study, the non-stationary fault diagnosis system based on full vector spectrum was constructed. The overall framework of fault diagnosis system based on full vector spectrum was devised. The major functions and technical features of the system were introduced.
引文
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