【作者】 周雁冰；

【导师】 柳亦兵；

【作者基本信息】 华北电力大学 ， 动力机械及工程， 2013， 博士

【摘要】 本文以齿轮传动系统故障振动信号为研究对象,在分析振动机理、故障模式基础上,从高斯性、非高斯性角度划分振动信号的各种成分,然后利用振动信号的双谱幅值提取到非高斯性强度、双谱熵这两类故障特征,最后讨论了新高阶统计量特征的优化与实用问题,主要研究成果如下：(1)通过研究齿轮传动系统主要零部件的失效模式、振动机理、故障表现,将振动信号划分为与故障部件有关的非高斯确定性成分、与故障部件无关的非高斯确定性成分、高斯随机成分、对称非高斯随机成分、非对称非高斯随机成分。当齿轮或轴承发生故障时,其振动信号中的确定性成分、随机成分会产生明显变化,于是信号的非高斯性也随之改变,尤其是因故障而增强的边带成分,它会导致信号的非高斯性、二次非线性增强,信号的双谱分析结果对此十分敏感。另外,双谱能抑制掉振动信号中的高斯随机成分、对称非高斯随机成分,保留非对称非高斯随机成分信息,这有利于降低噪声、非故障随机振动的干扰。(2)当齿轮传动系统发生故障时,振动信号的非高斯成分在双频域内的分布强度与形态均随之改变。基于双谱幅值信息,分别提取到量化描述振动信号非高斯成分强弱变化的非高斯性强度特征值,以及量化描述振动信号非高斯成分在双频域内分布形态变化的双谱熵特征值。按照双频域内的不同定义区间,共提取到六种特征值,分别是基于主定义域区间的NGIPD、HB-PD,基于任意二维区间part的NGIpart、HB-part,和基于多分区的NGIregion(k)、HB-region(k)。这些新的高阶统计量特征既保留了双谱分析的优点,而且弥补了双谱幅值切片等常规高阶统计量特征值的不足。不同定义区间的非高斯性强度、双谱熵特征值各有特点：NGIPD与HB-PD分别包含了双谱幅值的全部强度、分布形态信息,不含双谱对称冗余信息；当二维定义域区间part内包含有较丰富故障信息时,NGIpart与HB-part表征故障的能力较好,但需要人为观察并设定合适的双频域二维区间；振动信号的故障信息在双频域内分布不均,特征值NGIregion(k)、HB-region(k)在某些分区内故障特征提取效果较好,某些分区效果较差,这正体现了双频域分区特征值对双谱内容、故障信息具有较强的“聚焦能力"。(3)针对非高斯性强度、双谱熵特征值存在的不足,进行了特征压缩与信号滤波这两类故障特征优化。双谱分区特征值会产生高维特征空间,给实际应用造成麻烦,利用主分量分析、核函数主分量分析从线性及非线性角度进行特征压缩,可将绝大部分故障信息浓缩于低维主分量空间或低维核主分量空间,尤其在利用Fisher准则进行特征优选预处理后,新的主分量、核主分量特征值具有较高的故障区分能力。由于齿轮啮合频率及其谐波成分通常能量较强,且易受非故障因素影响,于是采用Gabor滤波与信号重构,在时频域空间精准滤除振动信号的啮合频率及其谐波成分,结果表明,重构信号的非高斯性强度特征对齿轮故障敏感程度提高,有利于后续故障诊断。另外,为研究新特征值在齿轮传动系统状态监测与故障报警中的使用方法与效果,以原始振动信号的HB-PD、Gabor滤波重构信号的NGIPD为例进行故障特征趋势分析,并根据“3σ准则”设定故障阈值,不论报警时间还是诊断准确率,都取得理想效果,为工程实际应用提供了新思路与新方法。更多还原

【Abstract】 This thesis took the fault vibration signals of gear transmission systems as the study objects. On the basis of analyzing the vibration mechanism and the failure modes, vibration signals were divided into various components from the perspective of Gaussian characteristic and non-Gaussian characteristic. Extracted-non-Gaussian intensity features and bispectral entropy features from bispectral amplitudes of vibration signals. Finally discussed the issues of feature optimization and practicality of new higher order statistics characteristics. The main achievements and results of this thesis are as follows:(1) By studying the failure modes, the vibration mechanism and the fault performance of gear transmission systems, vibration signals could be devided into several components such as the non-Gaussian deterministic components with the relevent of fault parts, the non-Gaussian deterministic components with the irrelevant of fault parts, the Gaussian random components, the symmetrical non-Gaussian random components and the asymmetric non-Gaussian random components. When gear or bearing is fault, the deterministic components and the random components of vibration signals would significantly change, and the non-Gaussian characteristic of the signals also changed. Especially the sideband component which was enhanced due to the fault, would cause the enhancement of non-Gaussian characteristic and quadratic nonlinear characteristic. The bispectral analysis results of the signals were very sensitive to this phenomenon. In addition, bispectrum can suppress the Gaussian random components and the symmetric non-Gaussian random components of vibration signals, meanwhile retain the information of asymmetric non-Gaussian random components. This was conducive to reducing the interference of noise components and non-faul random vibration components.(2) When a gear transmission system goes out of order, the distribution intensity and morphology of non-Gaussian components of vibration signals will change in bifrequency domain along with the fault. Based on bispectral amplitude information, the non-Gaussian intensity feature and the bispectral entropy feature can be extracted. Non-Gaussian intensity was the quantitative description of intensity change for the non-Gaussian components of vibration signals, while bispectral entropy was the quantitative description of distribution morphology change in bifrquency domain for the non-Gaussian components of vibration signals. According to the different bifrequency definition domains,6types of features were extracted. The features were NGIPD and HB-PD which were based on the principal domain, NGIpart and HB-part which were based on a arbitrary two-dimensional interval called’part’, NGIregion(k) and which were based on multi-partition. These new higher order statistics characteristics not only retained the advantages of bispectral analysis, but also made up for the lack of conventional higher order characteristics such as bispectral slices. Different non-Gaussian intensity and bispectral entropy features had their own characteristics. NGIPD and HB-PD respectively contained whole intensity information and whole distribution morphology information of bispectral amplitudes, excluded bispectral symmetry redundant information at the same time. If the two-dimensional interval’part’contained much abundant fault information, the abilities of characterizing failures of NGIpart and HN-part were better, but it required human observation and a appropriate two-dimensional interval setting in bifrequency domain. As a uneven distribution of vibration signals’fault information in bifrequency domain, the fault features extraction of NGIregion(k) and HB-region(k) were better in some regions, and worse in other regions, which reflected that features based on bifrequency patition had strong abilities to focusing on bispectral contents and fault information.(3) In order to remedying the shortcomings of non-Gaussian intensity and bispectral entropy features, made two types of characteristics optimization which were feature compression and signal filtering.. The bispectral partition features had high dimensional feature spaces, and caused trouble in the actual application. Made feature compression by principal component analysis and kernel principal component analysis from the linear or the non-linear perspective, and it can concentrate most of the fault information into low dimensional principal component space or low dimensional kernel principal component space. Especially after the features prioritization pretreatment according to the Fisher criterion, the new principal component features and the new kernel principal component features had higher fault distinguishing abilities. The gear meshing frequency and its harmonic components usually had high energy and be vulnerable to non-fault factors. So utilized Gabor filter and signal reconstruction to accurately exclude the meshing frequency and its harmonic components of vibration signals in time-frequency domain. The results showed that the non-Gaussian intensity features of reconstructed signals were more sensitive to gear faults. This was conducive to the follow-up fault diagnosis. In order to research the usage methods and the effects of the new features applied in condition monitoring and fault alarm for gear transmission systems, took the HB-PD of original signals and the NGIPD of reconstructed signals passing Gabor filter as examples, made trend analysis of these fault features and set fault threshold values according to the’3σ criterion’. Both of the alarm time and the diagnostic accuracy obtained ideal effects. It provided new ideas and new methods for the practical application of engineering.更多还原