多元统计分析在设备状态监测诊断中的应用研究
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摘要
本论文以设备状态的精确诊断为研究目标，着眼于多元统计分析在设备状态监测诊断中的应用研究，通过分析正在发展中的主分量分析(PCA)、独立分量分析(ICA)、核主分量分析(KPCA)和盲源分离(BSS)等四种多元统计分析方法在该应用领域中的研究现状，把此方面的研究统一于了三个子体系下：高阶统计信息提取、多元冗余特征融合、多维测量信号分离，并分别就有关问题进行了深入的研究。
第一个方面是基于ICA的理论，以一维或多维测量信号为处理对象，提取测量信号的高阶统计信息来有效表证设备的状态特征。本文主要引入并拓展了ICA提取一维振动信号高阶统计信息及其应用的研究，该高阶统计信息揭示了振动信号的本质属性，具有很好的应用前景。这里提出了一种新的基于ICA的瞬态检测方法，显示了优于其他传统方法的效果；提取了一种新的ICA基滤波相关特征参数，可以有效表征状态类别信息。
第二个方面是利用PCA、ICA和KPCA等分析方法的信息挖掘及降维作用，从时域、频域、时频域等多个原始特征中提取新的更加敏感稳健的统计结构，来表达和分类设备模式。本文主要研究了基于KPCA的非线性特征提取技术，提取的特征在特征空间具有非常好的聚类效果，然后研究了基于KPCA的非线性特征子空间构建，来有效表达和分类设备状态。本文还着重研究了多元统计特征提取中的特征评价和选取技术，提出了一些新的理论想法，解决了多元统计特征进行设备状态分类的效益最大化问题。
第三个方面是利用盲源分离(BSS及ICA)的思想对多维测量信号进行处理，以获得反映某个或每个设备部件的信号、分离提取某些信号分量、或者仅仅消去噪声的影响。本文基于盲源分离技术进行了设备多维振动信号的振动分量分离探讨，首先验证了线性ICA方法对复杂振动信号分离效果并不太理想，然后基于盲卷积分离模型主要研究了振动信号中瞬态分量(循环平稳成分、瞬时冲击成分等)与噪声分量的分离与提取。
此外，以上研究都建立在了实验验证基础上，本论文采用了两个实验，一个是通过振动信号分析的汽车齿轮箱的状态监测，另一个是运用噪声信号分析的内燃机轴承的磨损诊断。本文研究表明，多元统计分析能够提取反映设备状态的敏感可靠的特征信息，对精确诊断具有非常重要的意义。
With the research aim of precision diagnosis of machine conditions, this paper addresses on the application studies of multivariate statistical analysis for machine condition monitoring and diagnosis. Through analyzing the research literature of four developing multivariate statistical analysis methods including principal component analysis (PCA), independent component analysis (ICA), kernel principal component analysis (KPCA) and blind sources separation (BSS) in this research area, three sub-systems were built and studied in depth, respectively, as follows:
The first is higher-order statistical information extraction, which focuses on extracting the higher-order statistical information from the one-dimensional or multidimensional measured signals to effectively represent the machine conditions based on the ICA theory. This paper mainly introduced and developed the extraction of the higher-order statistical information of one-dimensional vibration signal, which has excellent potential applications since it reveals the inherent characteristics of vibration signals. A novel ICA-based transient detection method was proposed in this paper and showed the good effect outperforming the other traditional methods. In addition, a new ICA filtered correlation feature parameter was extracted to effectively represent the class information of machine conditions.
The second is redundant multivariate features fusion, which addresses on extracting the novel and more sensitive and more stable statistical structure from the original time-domain, frequency-domain and time-frequency domain features to represent and classify machine conditions, based on information compression and dimension reduction of PCA, ICA, KPCA, and so on. This paper mainly developed nonlinear feature extraction technique by using KPCA. The extracted nonlinear features have the excellent clustering effect in the feature space. Then the KPCA-based nonlinear feature subspace models were constructed to effectively represent and classify the machine conditions. This paper still emphasized the research of the feature evaluation and selection in multivariate statistical feature extraction, in which some new ideas were proposed to solve the problem of the maximum efficiency when the multivariate statistical features are used to recognize the machine conditions.
The third is multidimensional measured signals separation, which is to process the multidimensional measured signals by the idea of blind sources separation to obtain the signals reflecting the information of some or all machine components, separate and extract some signal components, or only eliminate the noise. This paper explored the vibration component separation of multidimensional machine vibration signals based on the BSS technique. The linear ICA method was validated to be not ideal when applied to the complex vibration signal separation. Then the BSS model for convolution mixtures was considered to mainly study the separation and extraction of transient components, including cyclostationary components and transient impulses, from the noisy signals.
Moreover, the research works above were all validated by the experiment analysis, in which two experiments were applied. One is the automobile gearbox condition monitoring by vibration signal analysis; the other is internal-combustion engine wear diagnosis by sound signal analysis. The study of this paper indicates that the multivariate statistical analysis can extract the sensitive and stable features that well represent machine conditions, which is very significant for precision diagnosis.
第一个方面是基于ICA的理论，以一维或多维测量信号为处理对象，提取测量信号的高阶统计信息来有效表证设备的状态特征。本文主要引入并拓展了ICA提取一维振动信号高阶统计信息及其应用的研究，该高阶统计信息揭示了振动信号的本质属性，具有很好的应用前景。这里提出了一种新的基于ICA的瞬态检测方法，显示了优于其他传统方法的效果；提取了一种新的ICA基滤波相关特征参数，可以有效表征状态类别信息。
第二个方面是利用PCA、ICA和KPCA等分析方法的信息挖掘及降维作用，从时域、频域、时频域等多个原始特征中提取新的更加敏感稳健的统计结构，来表达和分类设备模式。本文主要研究了基于KPCA的非线性特征提取技术，提取的特征在特征空间具有非常好的聚类效果，然后研究了基于KPCA的非线性特征子空间构建，来有效表达和分类设备状态。本文还着重研究了多元统计特征提取中的特征评价和选取技术，提出了一些新的理论想法，解决了多元统计特征进行设备状态分类的效益最大化问题。
第三个方面是利用盲源分离(BSS及ICA)的思想对多维测量信号进行处理，以获得反映某个或每个设备部件的信号、分离提取某些信号分量、或者仅仅消去噪声的影响。本文基于盲源分离技术进行了设备多维振动信号的振动分量分离探讨，首先验证了线性ICA方法对复杂振动信号分离效果并不太理想，然后基于盲卷积分离模型主要研究了振动信号中瞬态分量(循环平稳成分、瞬时冲击成分等)与噪声分量的分离与提取。
此外，以上研究都建立在了实验验证基础上，本论文采用了两个实验，一个是通过振动信号分析的汽车齿轮箱的状态监测，另一个是运用噪声信号分析的内燃机轴承的磨损诊断。本文研究表明，多元统计分析能够提取反映设备状态的敏感可靠的特征信息，对精确诊断具有非常重要的意义。
With the research aim of precision diagnosis of machine conditions, this paper addresses on the application studies of multivariate statistical analysis for machine condition monitoring and diagnosis. Through analyzing the research literature of four developing multivariate statistical analysis methods including principal component analysis (PCA), independent component analysis (ICA), kernel principal component analysis (KPCA) and blind sources separation (BSS) in this research area, three sub-systems were built and studied in depth, respectively, as follows:
The first is higher-order statistical information extraction, which focuses on extracting the higher-order statistical information from the one-dimensional or multidimensional measured signals to effectively represent the machine conditions based on the ICA theory. This paper mainly introduced and developed the extraction of the higher-order statistical information of one-dimensional vibration signal, which has excellent potential applications since it reveals the inherent characteristics of vibration signals. A novel ICA-based transient detection method was proposed in this paper and showed the good effect outperforming the other traditional methods. In addition, a new ICA filtered correlation feature parameter was extracted to effectively represent the class information of machine conditions.
The second is redundant multivariate features fusion, which addresses on extracting the novel and more sensitive and more stable statistical structure from the original time-domain, frequency-domain and time-frequency domain features to represent and classify machine conditions, based on information compression and dimension reduction of PCA, ICA, KPCA, and so on. This paper mainly developed nonlinear feature extraction technique by using KPCA. The extracted nonlinear features have the excellent clustering effect in the feature space. Then the KPCA-based nonlinear feature subspace models were constructed to effectively represent and classify the machine conditions. This paper still emphasized the research of the feature evaluation and selection in multivariate statistical feature extraction, in which some new ideas were proposed to solve the problem of the maximum efficiency when the multivariate statistical features are used to recognize the machine conditions.
The third is multidimensional measured signals separation, which is to process the multidimensional measured signals by the idea of blind sources separation to obtain the signals reflecting the information of some or all machine components, separate and extract some signal components, or only eliminate the noise. This paper explored the vibration component separation of multidimensional machine vibration signals based on the BSS technique. The linear ICA method was validated to be not ideal when applied to the complex vibration signal separation. Then the BSS model for convolution mixtures was considered to mainly study the separation and extraction of transient components, including cyclostationary components and transient impulses, from the noisy signals.
Moreover, the research works above were all validated by the experiment analysis, in which two experiments were applied. One is the automobile gearbox condition monitoring by vibration signal analysis; the other is internal-combustion engine wear diagnosis by sound signal analysis. The study of this paper indicates that the multivariate statistical analysis can extract the sensitive and stable features that well represent machine conditions, which is very significant for precision diagnosis.
引文
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