基于局域波法和盲源分离的故障诊断方法应用研究
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摘要
机械故障诊断是以机器学为基础的一门综合性技术,它的本质是一个机器运行状态的模式识别问题,其关键就是故障信号的特征提取与分类。近年来,为满足对机器故障进行早期检测与精确诊断的需要,非平稳、非高斯信号处理方法在机械故障诊断领域受到了广泛关注。为此,本文基于国家自然科学基金项目“局域波法及其工程应用研究”(50475155),利用非平稳、非高斯信号处理理论中的局域波法、Wigner高阶时频表示和盲源分离理论,结合模式识别与机器学习领域的研究成果,对非平稳、非高斯的机械振动信号特征提取与故障诊断问题进行了广泛而深入的研究。主要的工作如下:
1.应用局域波法对机械振动非平稳信号进行了研究。通过与小波变换和几种时频分析方法的比较,表明局域波法对于非平稳信号的分析更具有效性。实验结果表明,局域波时频分析能够清晰地表征不同故障的时变特征。由于局域波时频谱是一种二维的信号表示形式,在计算机对故障自动分类时,涉及到维数压缩的问题。为了用尽可能少的维数表示时频谱而不损失分类精度,几何矩和边缘分布可以作为时频分布的特征。在此基础上,结合人工神经网络,提出了一种基于局域波几何矩和边缘的故障诊断方法。
2.研究了基于局域波法的多分量神经网络预测模型的有效性,用于对非平稳系统时间序列进行建模。通过太阳黑子数据的仿真试验,验证了该多分量结构比对应的单一神经网络结构性能优越。最后根据该方法组成了一个自回归时间序列模型库,用于转子故障的模型诊断中。这些模型可以用做一步向前预测器,对检测和诊断信号进行比较。从预测误差提取特征,能够确定机器的状态。不同故障状态的转子振动信号用来训练和检验模型。实验数据表明,在适当训练样本长度下,这种方法用于故障诊断,可以实现故障的正确分类。
3.研究了高阶时频分布在振动冲击信号特征提取中的应用问题。在机械状态监控中,冲击信号的检测对于提取机器的状态信息是很有用的。通过Wigner高阶矩谱可以有效地对这样的非平稳、非高斯振动冲击信号进行特征提取和检测。针对高阶时频分布分析多分量信号时存在交叉项的问题,提出了一种利用局域波分解来减少Wigner高阶矩谱交叉项的方法,以仿真信号为例,验证了此方法的有效性;通过对现场测试的柴油机爆燃阶段信号的Wigner高阶矩谱分析,验证了该方法在机械故障特征提取中具有很好的应用潜力。最终表明,通过该方法可以提取有价值的关于冲击信号的时间和谱特性的量化信息。
基于局域波法和盲源分离的故障诊断方法应用研究
4.提出了一种基于局域波时频图像的盲源分离故障诊断方法。独立成分分析
(ICA)是实现盲源分离最有效的方法之一。ICA可以认为是PCA特征提取技术的推
广。ICA能够提供图像的局部特征,给出较好的图像表示。针对局域波时频图像可以表
征不同故障振动信号的特点,应用盲源分离技术对不同故障信号的局域波时频图像进行
独立分量分离,提取代表当前工况特征的投影系数矩阵,作为故障特征,利用神经网络
实现不同故障的自动分类。最后以转子的早期摩擦,基座松动,不对中故障振动信号为
例,应用该方法进行了研究,实验结果证明了该方法的可行性。
5.为了有效提取故障特征信号,需要在不同位置进行多传感器的振动信号测量。
针对多源混合的非平稳、非高斯设备故障振动信号,应用非平稳信号的盲源分离算法,
可以有效地提取各自独立的非平稳振动源,从而更加准确地进行机械故障诊断。首先,
针对不同时频分布的非平稳盲源分离算法,通过仿真信号比较了它们的分离效果。然后
以转子的复合故障为例进行了实验验证。在此基础上,提出了一种基于盲源分离的多传
感器数据融合故障诊断方法。实验结果证明该方法能够提高故障诊断的精度。
关键词:机械振动;故障诊断;局域波法;高阶时频分布;盲源分离
Mechinery diagnosis is a branch of mechanology. Its essence is the pattern recognition of machine operating condition. Its key issues are the feature extraction and classification for fault signals, hi recent years, to meet the needs of early detection and accurate diagnosis of mechanical faults, non-stationary and non-Gaussian signal processing techniques attracted more and more attention in mechanical fault feature extraction, In this dissertation, based on " Research on Local Wave method and its Engineering Application" (National nature science fund project. No:50475155 ), combined with the pattern recognition and machine learning techniques, the problems of feature extraction and fault diagnosis are addressed using Local Wave method, Wigner higher-order time-frequency representation and blind source separation of non-stationary, non-Gaussian vibration signals. The main contents as follows:1. Local-wave method is studied and applied to mechanical vibration non-stationary signal. By comparing with wavelet and several time-frequency methods, the Local-wave method can be proved to more effective than others. The experiment result shows that Local-wave time-frequency analysis can clearly explain time varying characteristics of different fault modes. But this time-frequency method is a two-dimensional signal representation. It arises the dimensionality problem. To describe the signal with as few variables as possible, the geometric moments and margins were used as the features of the time-frequency distribution. Then, combined with the artificial neural network, the fault diagnosis method is proposed based on geometric moments and marginal densities of Local-wave.2. The effectiveness of a multi-component neural-network architecture based on Local wave for the time series prediction of non-stationary, nonlinear dynamic systems has been investigated. The simulated experiment for sunspots' benchmark suggests that the multi-component architecture outperforms the corresponding single-scale architectures. Then, an observer bank of autoregressive time series models based on multi-component neural-network architecture is used for model diagnosis of rotor fault vibration signals. These models can be used as one step ahead predictors allowing comparison of signals for the purposes of fault detection and diagnosis. From the prediction error, features can be extracted and used to determine the machine's condition. Vibration data from a rotor placed under different fault conditions were used for training and testing models. The experiment results indicate that this approach could be used to diagnose fault conditions.3. Higher order time-frequency distributions and their applications to feature extraction of machine vibration signals are studied. The detection of these impulses can be useful for fault diagnosis purposes in condition monitoring environment. The features can be effectively
extracted for these non-stationary, non-Gaussian vibration signals by the Wigner higher order moment spectrum. The Local wave decomposition method is used to suppress the cross-term for multiple signals in higher order time-frequency distributions and the simulated results are satisfactory. The vibration signals measured from diesel engine in the stage of deflagrate are analyzed with Wigner higher order moment spectrum. Experimental results indicate that this method has good potential in mechanical fault feature extraction and can be used to extract the valuable quantifiable information about the time-frequency features of impacts.4. The blind source separation fault diagnosis method based on Local wave time-frequency images is developed. ICA has been mainly used on the problem of blind signal separation. ICA is a feature extraction technique which can be considered a generalization of principal component analysis (PCA). ICA can be used to gain the local features which give better image representation. Because the Local wave time-frequency image can reflect the character of different fault conditions, blind source separation as a new signal pr
1.应用局域波法对机械振动非平稳信号进行了研究。通过与小波变换和几种时频分析方法的比较,表明局域波法对于非平稳信号的分析更具有效性。实验结果表明,局域波时频分析能够清晰地表征不同故障的时变特征。由于局域波时频谱是一种二维的信号表示形式,在计算机对故障自动分类时,涉及到维数压缩的问题。为了用尽可能少的维数表示时频谱而不损失分类精度,几何矩和边缘分布可以作为时频分布的特征。在此基础上,结合人工神经网络,提出了一种基于局域波几何矩和边缘的故障诊断方法。
2.研究了基于局域波法的多分量神经网络预测模型的有效性,用于对非平稳系统时间序列进行建模。通过太阳黑子数据的仿真试验,验证了该多分量结构比对应的单一神经网络结构性能优越。最后根据该方法组成了一个自回归时间序列模型库,用于转子故障的模型诊断中。这些模型可以用做一步向前预测器,对检测和诊断信号进行比较。从预测误差提取特征,能够确定机器的状态。不同故障状态的转子振动信号用来训练和检验模型。实验数据表明,在适当训练样本长度下,这种方法用于故障诊断,可以实现故障的正确分类。
3.研究了高阶时频分布在振动冲击信号特征提取中的应用问题。在机械状态监控中,冲击信号的检测对于提取机器的状态信息是很有用的。通过Wigner高阶矩谱可以有效地对这样的非平稳、非高斯振动冲击信号进行特征提取和检测。针对高阶时频分布分析多分量信号时存在交叉项的问题,提出了一种利用局域波分解来减少Wigner高阶矩谱交叉项的方法,以仿真信号为例,验证了此方法的有效性;通过对现场测试的柴油机爆燃阶段信号的Wigner高阶矩谱分析,验证了该方法在机械故障特征提取中具有很好的应用潜力。最终表明,通过该方法可以提取有价值的关于冲击信号的时间和谱特性的量化信息。
基于局域波法和盲源分离的故障诊断方法应用研究
4.提出了一种基于局域波时频图像的盲源分离故障诊断方法。独立成分分析
(ICA)是实现盲源分离最有效的方法之一。ICA可以认为是PCA特征提取技术的推
广。ICA能够提供图像的局部特征,给出较好的图像表示。针对局域波时频图像可以表
征不同故障振动信号的特点,应用盲源分离技术对不同故障信号的局域波时频图像进行
独立分量分离,提取代表当前工况特征的投影系数矩阵,作为故障特征,利用神经网络
实现不同故障的自动分类。最后以转子的早期摩擦,基座松动,不对中故障振动信号为
例,应用该方法进行了研究,实验结果证明了该方法的可行性。
5.为了有效提取故障特征信号,需要在不同位置进行多传感器的振动信号测量。
针对多源混合的非平稳、非高斯设备故障振动信号,应用非平稳信号的盲源分离算法,
可以有效地提取各自独立的非平稳振动源,从而更加准确地进行机械故障诊断。首先,
针对不同时频分布的非平稳盲源分离算法,通过仿真信号比较了它们的分离效果。然后
以转子的复合故障为例进行了实验验证。在此基础上,提出了一种基于盲源分离的多传
感器数据融合故障诊断方法。实验结果证明该方法能够提高故障诊断的精度。
关键词:机械振动;故障诊断;局域波法;高阶时频分布;盲源分离
Mechinery diagnosis is a branch of mechanology. Its essence is the pattern recognition of machine operating condition. Its key issues are the feature extraction and classification for fault signals, hi recent years, to meet the needs of early detection and accurate diagnosis of mechanical faults, non-stationary and non-Gaussian signal processing techniques attracted more and more attention in mechanical fault feature extraction, In this dissertation, based on " Research on Local Wave method and its Engineering Application" (National nature science fund project. No:50475155 ), combined with the pattern recognition and machine learning techniques, the problems of feature extraction and fault diagnosis are addressed using Local Wave method, Wigner higher-order time-frequency representation and blind source separation of non-stationary, non-Gaussian vibration signals. The main contents as follows:1. Local-wave method is studied and applied to mechanical vibration non-stationary signal. By comparing with wavelet and several time-frequency methods, the Local-wave method can be proved to more effective than others. The experiment result shows that Local-wave time-frequency analysis can clearly explain time varying characteristics of different fault modes. But this time-frequency method is a two-dimensional signal representation. It arises the dimensionality problem. To describe the signal with as few variables as possible, the geometric moments and margins were used as the features of the time-frequency distribution. Then, combined with the artificial neural network, the fault diagnosis method is proposed based on geometric moments and marginal densities of Local-wave.2. The effectiveness of a multi-component neural-network architecture based on Local wave for the time series prediction of non-stationary, nonlinear dynamic systems has been investigated. The simulated experiment for sunspots' benchmark suggests that the multi-component architecture outperforms the corresponding single-scale architectures. Then, an observer bank of autoregressive time series models based on multi-component neural-network architecture is used for model diagnosis of rotor fault vibration signals. These models can be used as one step ahead predictors allowing comparison of signals for the purposes of fault detection and diagnosis. From the prediction error, features can be extracted and used to determine the machine's condition. Vibration data from a rotor placed under different fault conditions were used for training and testing models. The experiment results indicate that this approach could be used to diagnose fault conditions.3. Higher order time-frequency distributions and their applications to feature extraction of machine vibration signals are studied. The detection of these impulses can be useful for fault diagnosis purposes in condition monitoring environment. The features can be effectively
extracted for these non-stationary, non-Gaussian vibration signals by the Wigner higher order moment spectrum. The Local wave decomposition method is used to suppress the cross-term for multiple signals in higher order time-frequency distributions and the simulated results are satisfactory. The vibration signals measured from diesel engine in the stage of deflagrate are analyzed with Wigner higher order moment spectrum. Experimental results indicate that this method has good potential in mechanical fault feature extraction and can be used to extract the valuable quantifiable information about the time-frequency features of impacts.4. The blind source separation fault diagnosis method based on Local wave time-frequency images is developed. ICA has been mainly used on the problem of blind signal separation. ICA is a feature extraction technique which can be considered a generalization of principal component analysis (PCA). ICA can be used to gain the local features which give better image representation. Because the Local wave time-frequency image can reflect the character of different fault conditions, blind source separation as a new signal pr
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