基于改进支持向量机和纹理图像分析的旋转机械故障诊断
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摘要
随着机械设备不断向高速化、重载化和复杂化方向发展,人们对其运行可靠性和安全性的要求也越来越高。采用有效的模式识别方法实现机械设备的高精度智能故障诊断,对保障设备的高效运行和安全生产具有重要意义。本文以轴承和刀具为对象,围绕故障分类识别和故障特征提取这两个核心问题,重点研究基于改进支持向量机的故障分类方法和基于纹理分析的故障特征提取方法及其在工程实际中的应用。本文研究内容和成果主要体现在以下几方面:
1)基于支持向量机的智能故障诊断方法是目前研究的热点。为克服模型参数选择随意性对支持向量机分类性能的不利影响,提出了基于人工蜂群算法优化的支持向量机故障分类方法,并将其应用于滚动轴承的智能故障诊断。该方法以分类错误率的倒数作为适应度函数,利用人工蜂群算法对支持向量机参数进行优化选择。通过UCI标准数据集验证,证明本文提出的方法兼顾了对局部最优解和全局最优解的搜索,克服了传统优化方法易陷入局部最优解的缺陷,获得了更高的识别正确率,并且在小数目分类问题上有效地降低了搜索最优参数所需的时间。将该方法应用于实测轴承故障数据实验,获得了较高的故障识别正确率。
2)刀具磨损状态的自动识别是一种小样本条件下的模式识别问题,在特定的加工条件下只能获取非常有限的训练样本。针对这一问题,提出一种基于改进超球面支持向量机的故障分类方法,并将其应用于刀具磨损状态的自动识别。该方法提取切削力与振动信号中的多项特征,对各项特征分别进行刀具磨损量相关性分析,选择与刀具磨损变化量最相关的均值、均方根、小波系数能量以及小波系数近似熵组成特征向量。在分类器方面,考虑到各类样本的疏松程度不同,利用引力法对超球面支持向量机的决策函数进行改进,经过优化分析得到最佳分类引力公式。采用改进的超球面支持向量机作为分类器,实现了刀具磨损状态的自动识别。实验证明,在小样本学习情况下,基于改进超球面支持向量机的刀具磨损状态识别方法具有良好的学习和泛化能力,可获得较高的识别正确率。
3)作为故障信息多维特征提取方法的基础,首先对基于支持向量机的纹理分析方法进行研究。针对纹理图像分割中的训练样本自动获取问题,提出了一种基于模糊C均值算法的支持向量机半监督图像分割方法。该方法首先采用改进的Laws能量测度法对原始图像进行特征提取,对获取的特征图像进行分块处理以获得若干窗口,采用模糊C均值算法对图像平滑区域所在窗口的特征向量进行分类并获取类别标记,将特征向量和获取的类别标记作为模糊支持向量机的训练样本,从而实现了训练样本的自动获取。进而利用训练好的模糊支持向量机对非平滑区域进行精细分类。最终将模糊C均值和模糊支持向量机的分类结果组合形成最终的分类结果图像。采用随机选取的Brodatz纹理集中的纹理图像对上述算法进行测试,得到了较高的分割正确率,从而验证了本文算法的有效性,并为下一章时频分布图二维故障特征提取算法的研究提供理论基础。
4)特征提取对故障诊断的分类结果具有至关重要的影响。S变换得到的等高线时频图和Hilbert-Huang变换得到的Hilbert谱时频图包含了丰富的二维图像信息。在纹理图像自动分割方法的研究基础上,探讨了将图像处理领域中的纹理分析方法应用于一维信号时频分布图的故障特征提取问题。首先对灰度化的S变换等高线时频图和Hilbert谱时频图进行二维离散小波变换,利用Laws能量测度法计算各频道小波图像的灰度均方差作为能量特征并组成特征向量,从而建立了有效的从一维原始信号到二维纹理图像特征的映射模型。以支持向量机作为分类器,通过滚动轴承实测故障数据对上述方法进行验证,实验结果证明了上述特征提取算法的有效性和可行性。
5)针对目前国内高档数控机床对状态监测与故障诊断功能的需求,研究开发网络架构下机床整机智能监测诊断试验平台。重点针对笔者开发的强耦合状态监测单元信号采集技术、远程机床状态监测与故障诊断系统软件功能以及实现二者无缝信息交互的TDNC-Connect传输协议三方面进行阐述,构建出嵌入支持向量机智能故障诊断方法的网络架构下机床整机智能监测诊断试验平台,一体化地实现高档数控机床的状态可显示、性能可预报、故障可诊断、远程可监控。最终在工程实际中验证了该平台的可靠性与有效性。
People's demand for operational reliability and safety of the mechanical equipment was higher and higher with the development of the mechanical equipment towards high speed, heavy load and complication. It was very significant for equipment's efficient operation and manufacture safety to implement high precision intelligent fault diagnosis with effective pattern recognition methods. Taking the bearing and tool as the study object, Research was carried out around the two core problem of fault classification and fault feature extraction. The key problem were the fault classification methods based on the improved support vector machine and the fault feature extraction methods based on texture analysis, and the engineering application of these methods.
The content and results in this paper is as follows:
1) Fault diagnosis methods based on support vector machine was the research hotspot. To overcome the adverse effects of randomicity of model parameters, a parameter optimized support vector machine was proposed, which was based on artificial bee colony algorithm, and it is applied to bearing fault diagnosis. In this method, the inverse of classification error rate is used as fitness value, and the artificial bee colony algorithm is used to optimize the penalty factor and kernel parameter of support vector machine. Through the test of UCI dataset, it is proved that the proposed method, taking both the local optimal solution search and global optimal solution search into account, overcomes the defect that traditional optimization method tends to stuck in local optimal solution, and high recognition rate is acquired. The cost time of searching optimized parameters of small number classification problem is also reduced. At last, the proposed method is used in bearing fault diagnosis experiment, and high recognition rate is acquired.
2) Tool wear state recognition was a pattern recognition problem under the small samples condition. Only a small quantity of training samples could be acquired under the specific processing condition. To solve the problem, A tool wear state recognition method based on improved hyper-sphere support vector machine was proposed after research. In this method, features were extracted from cutting force signal and vibration acceleration signal, and through correlation analysis the ultima feature vectors were composed of the mean value, RMS, the energy value and the approximate entropy of low frequency band obtained from wavelet transform. In the aspect of classification algorithm, considering the difference of samples’distribution, gravitation method was used to improve the decision function of hyper-sphere support vector machine for acquiring the optimized classification formula. The improved hyper-sphere support vector machine was adopted as classifier to implement tool wear state automatic recognition. Prove by experiment, the proposed method based on hyper-sphere support vector machine had great generalization and learning ability, and high recognition rate could be achieved.
3) As the basis of multi-dimension fault information feature extraction method, the texture analysis method based on support vector machine was researched. A support vector machine half-supervised machine learning method based on fuzzy C-mean algorithm objective to feature vector automatic acquirement was proposed and used in texture image segmentation. In this method, an improved Laws energy measure method was adopted as feature extraction method. Feature image was segmented into several blocks and fuzzy C-mean algorithm was adopted to classify the pixels’feature vector in smooth block and to acquire the class marks. The feature vectors and class marks were treated as the training samples of fuzzy support vector machine to implement automation of training sample acquirement. Then the trained support vector machine was used to classify the feature vectors in unsmooth block to implement the high precision texture segmentation. The final classification image was composed of the classification marks of fuzzy C-mean algorithm and fuzzy support vector machine. Some texture image from Brodatz set was chosen to test the proposed method, and high precision rate was achieved. The theoretical basis of time-frequency distribution map feature extraction algorithm in next chapter was also provided.
4) Feature extraction played an important role in fault diagnosis classification. The time-frequency contour map by S transform and Hilbert spectrum map by Hilbert-Huang transform contained rich two-dimension information. Base on the research of texture image segmentation method in previous chapter, the fault feature extraction problem, which was about how to apply the texture analysis method in image processing field to one-dimension signal’s time-frequency distribution map feature extraction, was discussed in this chapter. Firstly, two-dimension discrete wavelet transform was applied to time-frequency contour gray image by S transform and Hilbert spectrum gray image by Hilbert-Huang transform, then Laws energy measure method was used to calculate the mean square deviation of wavelet image of every channel as the feature, then the features were composed into feature vector. As a result, an effective mapping model corresponding one-dimension signal to two-dimension texture feature was built. At last, using the support vector machine as classifier, the feature extraction method was approved effective and feasible through bearing fault diagnosis experiment.
5) Directing towards the function requirement of condition monitoring and fault diagnosis on high-grade numerical control machine, research and development on networking numerical control machine overall unit intelligent monitoring and diagnosis experiment platform was made. The author's main work of signal acquisition technology on the strong coupling condition monitoring unit, software function of remote numerical control machine condition monitoring and fault diagnosis system and TDNC-Connect transport protocol for seamless information interaction were expounded. The purpose was constructing the networking numerical control machine overall unit intelligent monitoring and diagnosis experiment platform with support vector machine fault classifier. Through the platform the function of condition display, performance forecast, fault diagnosis and remote monitoring aiming at numerical control machine were implemented. The reliability and effectiveness of this platform was proved through engineering application.
1)基于支持向量机的智能故障诊断方法是目前研究的热点。为克服模型参数选择随意性对支持向量机分类性能的不利影响,提出了基于人工蜂群算法优化的支持向量机故障分类方法,并将其应用于滚动轴承的智能故障诊断。该方法以分类错误率的倒数作为适应度函数,利用人工蜂群算法对支持向量机参数进行优化选择。通过UCI标准数据集验证,证明本文提出的方法兼顾了对局部最优解和全局最优解的搜索,克服了传统优化方法易陷入局部最优解的缺陷,获得了更高的识别正确率,并且在小数目分类问题上有效地降低了搜索最优参数所需的时间。将该方法应用于实测轴承故障数据实验,获得了较高的故障识别正确率。
2)刀具磨损状态的自动识别是一种小样本条件下的模式识别问题,在特定的加工条件下只能获取非常有限的训练样本。针对这一问题,提出一种基于改进超球面支持向量机的故障分类方法,并将其应用于刀具磨损状态的自动识别。该方法提取切削力与振动信号中的多项特征,对各项特征分别进行刀具磨损量相关性分析,选择与刀具磨损变化量最相关的均值、均方根、小波系数能量以及小波系数近似熵组成特征向量。在分类器方面,考虑到各类样本的疏松程度不同,利用引力法对超球面支持向量机的决策函数进行改进,经过优化分析得到最佳分类引力公式。采用改进的超球面支持向量机作为分类器,实现了刀具磨损状态的自动识别。实验证明,在小样本学习情况下,基于改进超球面支持向量机的刀具磨损状态识别方法具有良好的学习和泛化能力,可获得较高的识别正确率。
3)作为故障信息多维特征提取方法的基础,首先对基于支持向量机的纹理分析方法进行研究。针对纹理图像分割中的训练样本自动获取问题,提出了一种基于模糊C均值算法的支持向量机半监督图像分割方法。该方法首先采用改进的Laws能量测度法对原始图像进行特征提取,对获取的特征图像进行分块处理以获得若干窗口,采用模糊C均值算法对图像平滑区域所在窗口的特征向量进行分类并获取类别标记,将特征向量和获取的类别标记作为模糊支持向量机的训练样本,从而实现了训练样本的自动获取。进而利用训练好的模糊支持向量机对非平滑区域进行精细分类。最终将模糊C均值和模糊支持向量机的分类结果组合形成最终的分类结果图像。采用随机选取的Brodatz纹理集中的纹理图像对上述算法进行测试,得到了较高的分割正确率,从而验证了本文算法的有效性,并为下一章时频分布图二维故障特征提取算法的研究提供理论基础。
4)特征提取对故障诊断的分类结果具有至关重要的影响。S变换得到的等高线时频图和Hilbert-Huang变换得到的Hilbert谱时频图包含了丰富的二维图像信息。在纹理图像自动分割方法的研究基础上,探讨了将图像处理领域中的纹理分析方法应用于一维信号时频分布图的故障特征提取问题。首先对灰度化的S变换等高线时频图和Hilbert谱时频图进行二维离散小波变换,利用Laws能量测度法计算各频道小波图像的灰度均方差作为能量特征并组成特征向量,从而建立了有效的从一维原始信号到二维纹理图像特征的映射模型。以支持向量机作为分类器,通过滚动轴承实测故障数据对上述方法进行验证,实验结果证明了上述特征提取算法的有效性和可行性。
5)针对目前国内高档数控机床对状态监测与故障诊断功能的需求,研究开发网络架构下机床整机智能监测诊断试验平台。重点针对笔者开发的强耦合状态监测单元信号采集技术、远程机床状态监测与故障诊断系统软件功能以及实现二者无缝信息交互的TDNC-Connect传输协议三方面进行阐述,构建出嵌入支持向量机智能故障诊断方法的网络架构下机床整机智能监测诊断试验平台,一体化地实现高档数控机床的状态可显示、性能可预报、故障可诊断、远程可监控。最终在工程实际中验证了该平台的可靠性与有效性。
People's demand for operational reliability and safety of the mechanical equipment was higher and higher with the development of the mechanical equipment towards high speed, heavy load and complication. It was very significant for equipment's efficient operation and manufacture safety to implement high precision intelligent fault diagnosis with effective pattern recognition methods. Taking the bearing and tool as the study object, Research was carried out around the two core problem of fault classification and fault feature extraction. The key problem were the fault classification methods based on the improved support vector machine and the fault feature extraction methods based on texture analysis, and the engineering application of these methods.
The content and results in this paper is as follows:
1) Fault diagnosis methods based on support vector machine was the research hotspot. To overcome the adverse effects of randomicity of model parameters, a parameter optimized support vector machine was proposed, which was based on artificial bee colony algorithm, and it is applied to bearing fault diagnosis. In this method, the inverse of classification error rate is used as fitness value, and the artificial bee colony algorithm is used to optimize the penalty factor and kernel parameter of support vector machine. Through the test of UCI dataset, it is proved that the proposed method, taking both the local optimal solution search and global optimal solution search into account, overcomes the defect that traditional optimization method tends to stuck in local optimal solution, and high recognition rate is acquired. The cost time of searching optimized parameters of small number classification problem is also reduced. At last, the proposed method is used in bearing fault diagnosis experiment, and high recognition rate is acquired.
2) Tool wear state recognition was a pattern recognition problem under the small samples condition. Only a small quantity of training samples could be acquired under the specific processing condition. To solve the problem, A tool wear state recognition method based on improved hyper-sphere support vector machine was proposed after research. In this method, features were extracted from cutting force signal and vibration acceleration signal, and through correlation analysis the ultima feature vectors were composed of the mean value, RMS, the energy value and the approximate entropy of low frequency band obtained from wavelet transform. In the aspect of classification algorithm, considering the difference of samples’distribution, gravitation method was used to improve the decision function of hyper-sphere support vector machine for acquiring the optimized classification formula. The improved hyper-sphere support vector machine was adopted as classifier to implement tool wear state automatic recognition. Prove by experiment, the proposed method based on hyper-sphere support vector machine had great generalization and learning ability, and high recognition rate could be achieved.
3) As the basis of multi-dimension fault information feature extraction method, the texture analysis method based on support vector machine was researched. A support vector machine half-supervised machine learning method based on fuzzy C-mean algorithm objective to feature vector automatic acquirement was proposed and used in texture image segmentation. In this method, an improved Laws energy measure method was adopted as feature extraction method. Feature image was segmented into several blocks and fuzzy C-mean algorithm was adopted to classify the pixels’feature vector in smooth block and to acquire the class marks. The feature vectors and class marks were treated as the training samples of fuzzy support vector machine to implement automation of training sample acquirement. Then the trained support vector machine was used to classify the feature vectors in unsmooth block to implement the high precision texture segmentation. The final classification image was composed of the classification marks of fuzzy C-mean algorithm and fuzzy support vector machine. Some texture image from Brodatz set was chosen to test the proposed method, and high precision rate was achieved. The theoretical basis of time-frequency distribution map feature extraction algorithm in next chapter was also provided.
4) Feature extraction played an important role in fault diagnosis classification. The time-frequency contour map by S transform and Hilbert spectrum map by Hilbert-Huang transform contained rich two-dimension information. Base on the research of texture image segmentation method in previous chapter, the fault feature extraction problem, which was about how to apply the texture analysis method in image processing field to one-dimension signal’s time-frequency distribution map feature extraction, was discussed in this chapter. Firstly, two-dimension discrete wavelet transform was applied to time-frequency contour gray image by S transform and Hilbert spectrum gray image by Hilbert-Huang transform, then Laws energy measure method was used to calculate the mean square deviation of wavelet image of every channel as the feature, then the features were composed into feature vector. As a result, an effective mapping model corresponding one-dimension signal to two-dimension texture feature was built. At last, using the support vector machine as classifier, the feature extraction method was approved effective and feasible through bearing fault diagnosis experiment.
5) Directing towards the function requirement of condition monitoring and fault diagnosis on high-grade numerical control machine, research and development on networking numerical control machine overall unit intelligent monitoring and diagnosis experiment platform was made. The author's main work of signal acquisition technology on the strong coupling condition monitoring unit, software function of remote numerical control machine condition monitoring and fault diagnosis system and TDNC-Connect transport protocol for seamless information interaction were expounded. The purpose was constructing the networking numerical control machine overall unit intelligent monitoring and diagnosis experiment platform with support vector machine fault classifier. Through the platform the function of condition display, performance forecast, fault diagnosis and remote monitoring aiming at numerical control machine were implemented. The reliability and effectiveness of this platform was proved through engineering application.
引文
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