旋转机械振动故障诊断的图形识别方法研究
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摘要
结合国家自然科学基金课题“基于数学形态学图形识别的旋转机械故障诊断方法”和课题组承担的实际应用课题“大型旋转机械故障诊断技术研究”,研究了基于数学形态学图形识别技术的旋转机械振动故障诊断方法。
为了应用三维谱图进行旋转机械故障诊断,从图形识别的角度出发,在详细分析三维谱图纹理特征的基础上,根据旋转机械周期激振的特点,研究了利用转子动力学理论构造其振动响应基函数对图形进行插值重构处理的方法,在理论研究的基础上,开展了汽轮机三维谱图的插值预处理的应用研究,结果表明所提出的图形插值预处理方法具有良好图形处理效果。
在分析旋转机械振动三维谱图中各倍频及亚次倍频处出现的随转速起伏变化“峰谷”特征及倍频之间幅值的悬殊变化规律的基础上,研究了基于正弦函数的复合灰度非线性量化算法,实现了故障敏感区域的灰度值加强,提高了图形的信噪比。为了使处理后的图形灰度直方图在较大的动态范围内趋于均衡,提出了自适应直方图均衡化的图形增强方法,提高图形整体对比度,扩大了像素灰度值的动态范围,改善了图形灰度分布的概貌,增强了图形的辨析程度。应用所提出的量化、增强方法对旋转机械实际图形进行了实例分析。
在分析旋转机械灰度图形纹理的特点、现有图形边缘纹理提取方法的局限性的基础上,提出了图形边缘纹理检测的模糊软形态学方法,构造了模糊软形态滤波增强算子,平滑图形的轮廓、消除图形边缘毛刺和孤立点及滤除图形背景上的噪声等,有效的对图形进行了滤波增强;设计了适合于旋转机械灰度图形处理的模糊软结构元素,提取了边缘纹理特征,确定了图形的几何与拓扑结构,并进行了仿真和实例算法分析。
针对旋转机械参数图形的特点,研究了基于纹理的统计法、结构法及表征图形纹理方向的梯度方法的灰度-梯度-基元三维共生矩阵描述图形纹理数字特征方法,精确地反映了图形纹理的粗糙程度、重复方向和空间复杂度及纹理方向;准确地描述了图形灰度空间分布特性(概率)、空间统计相关性和图形内各像素点梯度的分布规律。描述了灰度统计和空间结构的纹理特征,有效地提取旋转机械状态参数图形中纹理特征信息。
在对免疫系统反面选择机理及现有反面选择算法进行分析的基础上,提出了适于旋转机械振动参数图形识别的可变实阈值免疫反面选择算法。研究了检测器变异算法、检测器的检测半径、数量等参数的确定准则,应用可变实阈值免疫反面选择诊断方法分别对Iris模式识别数据和汽轮机故障数据进行了诊断分析,结果表明所提出的方法能准确地检测出汽轮机的各种故障,取得了较好的应用效果。
为了克服任何单一性质故障特征、单一诊断方法难以实现在整个故障状态空间上准确诊断的局限性,提出了基于遗传算法的融合诊断方法,充分利用了各种不同性质故障特征和不同诊断方法,使其发挥各自的优点,从而提高了诊断的准确率。提出了利用遗传算法将人工免疫诊断方法、神经网络诊断方法及小波包特征、双谱特征融合起来进行诊断,使每一个诊断方法及故障特征都在其优势空间发挥作用,并结合实际汽轮机样本进行融合诊断分析。
为了验证本文提出的基于图形识别技术的诊断方法的有效性,在600MW超临界模化汽轮机转子实验台上进行了正常状态和转子不平衡、转子不对中、汽流激振、轴承松动及动静碰磨等故障的实验研究,结合本文所提出的基于图形识别的诊断方法对图形样本进行了诊断,获得较高的准确率。
Combined with the project of National Natural Science Foundation of China Fault Diagnosis Method Based on Mathematical Morphology Graphic Recognition of Rotating Machinery and the practical project named Study of Fault Diagnosis Technology of Large-scale Rotating Machinery, the approaches of fault diagnosis based on the mathematical morphology graphic recognition for rotating machinery is investigated in this dissertation.
In order to apply three-dimensional spectrum to diagnose the fault of rotating machinery, from the perspective of graphic recognition, based on the detailed analysis of graphic texture characteristic, according to the characteristic of periodic excited of the rotating machinery, the method of applying rotor dynamics theory to construct the basic function of the vibration response so as to deal with graph by means of interpolation reconstruction treatment is studied. On the basis of theoretical research, the application research of pretreatment interpolation of the three-dimensional spectrum graph of turbine is developed, the results indicates that the effect is good when applying the proposed method to treating the graph.
On the basis of the analysis of changing law that the amplitude ranges rise and fall with the changes of rotating speed in the three-dimensional vibration graph of rotating machinery. The complex non-linear gray-scale algorithm based on the sine function is studied. Gray scale value of the region which is sensitive to fault is strengthend, improved signal-to-noise ratio. In order to make the treated gray scale histogram tend to be balanced in great dynamic range, the adaptive histogram equalization method is proposed to enhance the graph, improve the overall graphic contrast ratio, expand the dynamic range of pixel grayscale vale and improve graphic grayscale distribution profile. Example analysis on actual graph of rotating machinery is carried on by means of the proposed quantization and enhanced method.
On the basis of the analysis of grayscale graphics of rotating machinery and the limitations of extraction method of the texture of the existing graphics edge, the fuzzy soft morphology method of the graph edge texture extraction is proposed. The fuzzy soft morphology filter is constructed to smooth the graph outline, eliminate the graph edge burr and the isolated point, filter the graph background noise and so on. The fuzzy soft structural element that is suited to processing the grayscale graphic for rotating machinery is designed. The edge textural property is extracted, the graph geometrical and topological structure is determined, and the simulation and the instantial algorithmic analysis are carried on.
In view of the rotating mechanical parameter graph characteristic, the grayscale-gradient-primitive three dimensional co-occurrence matrix based on the statistical method, the structural method based on texture and the gradient method which characterize the graphic textural direction is studied for describing graphic textural digital characteristic. The rough degree, direction and spatial complex degree and direction of texture are reflected precisely. The graphic grayscale spatial distribution characteristic, spatial statistical dependence and pixel point gradient distributed rule are described accurately. Textural feature information in rotating mechanical state parameter graph is extracted effectively.
On the basis of the analysis of the immune negative selection mechanism and the existing negative selection algorithm, variable real threshold immune negative selection algorithm which is suited to rotating machinery vibration parameter graph recognition. The detector’s mutation algorithm, are studied. The variable real threshold immue negative selection diagnosis method is applied to analyze the“Iris”data and fault data of turbine. The result shows that the proposed method can detect various faults of turbine accurately.
In order to overcome the limitation that the single nature fault characteristic and the single diagnosis method are difficult to be diagnosed accurately in the entire fault state space, the integration diagnosis method based on genetic algorithm is proposed, faults characteristic of different property and different diagnosis methods are fully used to exert each one’s advantages, so the accuracy of diagnosis is increased. In this paper, a weighted matrix is established by integrating neural network and artificial immune diagnoses, Wavelet Packet energy and Bispectrum features using genetic algorithm. Experimental results indicate that both diagnosis accuracy and robustness of diagnosis system can be improved by the method.
In order to verify the effectiveness of the proposed diagnosis method based on graphic recognition technology, experimental study is carried out on 600MW supercritical modeling turbine rotor bearing system for test-bed, simulating normal state and different faults such as rotor’s unbalance, rotors’misalignment, steam-excited vibration, bear’s looseness, dynamic static rub and impact ,and so on. Relatively higher accuracy is obtained, when diagnosing faults with diagnosis method based on graphic recognition which is proposed in the article.
为了应用三维谱图进行旋转机械故障诊断,从图形识别的角度出发,在详细分析三维谱图纹理特征的基础上,根据旋转机械周期激振的特点,研究了利用转子动力学理论构造其振动响应基函数对图形进行插值重构处理的方法,在理论研究的基础上,开展了汽轮机三维谱图的插值预处理的应用研究,结果表明所提出的图形插值预处理方法具有良好图形处理效果。
在分析旋转机械振动三维谱图中各倍频及亚次倍频处出现的随转速起伏变化“峰谷”特征及倍频之间幅值的悬殊变化规律的基础上,研究了基于正弦函数的复合灰度非线性量化算法,实现了故障敏感区域的灰度值加强,提高了图形的信噪比。为了使处理后的图形灰度直方图在较大的动态范围内趋于均衡,提出了自适应直方图均衡化的图形增强方法,提高图形整体对比度,扩大了像素灰度值的动态范围,改善了图形灰度分布的概貌,增强了图形的辨析程度。应用所提出的量化、增强方法对旋转机械实际图形进行了实例分析。
在分析旋转机械灰度图形纹理的特点、现有图形边缘纹理提取方法的局限性的基础上,提出了图形边缘纹理检测的模糊软形态学方法,构造了模糊软形态滤波增强算子,平滑图形的轮廓、消除图形边缘毛刺和孤立点及滤除图形背景上的噪声等,有效的对图形进行了滤波增强;设计了适合于旋转机械灰度图形处理的模糊软结构元素,提取了边缘纹理特征,确定了图形的几何与拓扑结构,并进行了仿真和实例算法分析。
针对旋转机械参数图形的特点,研究了基于纹理的统计法、结构法及表征图形纹理方向的梯度方法的灰度-梯度-基元三维共生矩阵描述图形纹理数字特征方法,精确地反映了图形纹理的粗糙程度、重复方向和空间复杂度及纹理方向;准确地描述了图形灰度空间分布特性(概率)、空间统计相关性和图形内各像素点梯度的分布规律。描述了灰度统计和空间结构的纹理特征,有效地提取旋转机械状态参数图形中纹理特征信息。
在对免疫系统反面选择机理及现有反面选择算法进行分析的基础上,提出了适于旋转机械振动参数图形识别的可变实阈值免疫反面选择算法。研究了检测器变异算法、检测器的检测半径、数量等参数的确定准则,应用可变实阈值免疫反面选择诊断方法分别对Iris模式识别数据和汽轮机故障数据进行了诊断分析,结果表明所提出的方法能准确地检测出汽轮机的各种故障,取得了较好的应用效果。
为了克服任何单一性质故障特征、单一诊断方法难以实现在整个故障状态空间上准确诊断的局限性,提出了基于遗传算法的融合诊断方法,充分利用了各种不同性质故障特征和不同诊断方法,使其发挥各自的优点,从而提高了诊断的准确率。提出了利用遗传算法将人工免疫诊断方法、神经网络诊断方法及小波包特征、双谱特征融合起来进行诊断,使每一个诊断方法及故障特征都在其优势空间发挥作用,并结合实际汽轮机样本进行融合诊断分析。
为了验证本文提出的基于图形识别技术的诊断方法的有效性,在600MW超临界模化汽轮机转子实验台上进行了正常状态和转子不平衡、转子不对中、汽流激振、轴承松动及动静碰磨等故障的实验研究,结合本文所提出的基于图形识别的诊断方法对图形样本进行了诊断,获得较高的准确率。
Combined with the project of National Natural Science Foundation of China Fault Diagnosis Method Based on Mathematical Morphology Graphic Recognition of Rotating Machinery and the practical project named Study of Fault Diagnosis Technology of Large-scale Rotating Machinery, the approaches of fault diagnosis based on the mathematical morphology graphic recognition for rotating machinery is investigated in this dissertation.
In order to apply three-dimensional spectrum to diagnose the fault of rotating machinery, from the perspective of graphic recognition, based on the detailed analysis of graphic texture characteristic, according to the characteristic of periodic excited of the rotating machinery, the method of applying rotor dynamics theory to construct the basic function of the vibration response so as to deal with graph by means of interpolation reconstruction treatment is studied. On the basis of theoretical research, the application research of pretreatment interpolation of the three-dimensional spectrum graph of turbine is developed, the results indicates that the effect is good when applying the proposed method to treating the graph.
On the basis of the analysis of changing law that the amplitude ranges rise and fall with the changes of rotating speed in the three-dimensional vibration graph of rotating machinery. The complex non-linear gray-scale algorithm based on the sine function is studied. Gray scale value of the region which is sensitive to fault is strengthend, improved signal-to-noise ratio. In order to make the treated gray scale histogram tend to be balanced in great dynamic range, the adaptive histogram equalization method is proposed to enhance the graph, improve the overall graphic contrast ratio, expand the dynamic range of pixel grayscale vale and improve graphic grayscale distribution profile. Example analysis on actual graph of rotating machinery is carried on by means of the proposed quantization and enhanced method.
On the basis of the analysis of grayscale graphics of rotating machinery and the limitations of extraction method of the texture of the existing graphics edge, the fuzzy soft morphology method of the graph edge texture extraction is proposed. The fuzzy soft morphology filter is constructed to smooth the graph outline, eliminate the graph edge burr and the isolated point, filter the graph background noise and so on. The fuzzy soft structural element that is suited to processing the grayscale graphic for rotating machinery is designed. The edge textural property is extracted, the graph geometrical and topological structure is determined, and the simulation and the instantial algorithmic analysis are carried on.
In view of the rotating mechanical parameter graph characteristic, the grayscale-gradient-primitive three dimensional co-occurrence matrix based on the statistical method, the structural method based on texture and the gradient method which characterize the graphic textural direction is studied for describing graphic textural digital characteristic. The rough degree, direction and spatial complex degree and direction of texture are reflected precisely. The graphic grayscale spatial distribution characteristic, spatial statistical dependence and pixel point gradient distributed rule are described accurately. Textural feature information in rotating mechanical state parameter graph is extracted effectively.
On the basis of the analysis of the immune negative selection mechanism and the existing negative selection algorithm, variable real threshold immune negative selection algorithm which is suited to rotating machinery vibration parameter graph recognition. The detector’s mutation algorithm, are studied. The variable real threshold immue negative selection diagnosis method is applied to analyze the“Iris”data and fault data of turbine. The result shows that the proposed method can detect various faults of turbine accurately.
In order to overcome the limitation that the single nature fault characteristic and the single diagnosis method are difficult to be diagnosed accurately in the entire fault state space, the integration diagnosis method based on genetic algorithm is proposed, faults characteristic of different property and different diagnosis methods are fully used to exert each one’s advantages, so the accuracy of diagnosis is increased. In this paper, a weighted matrix is established by integrating neural network and artificial immune diagnoses, Wavelet Packet energy and Bispectrum features using genetic algorithm. Experimental results indicate that both diagnosis accuracy and robustness of diagnosis system can be improved by the method.
In order to verify the effectiveness of the proposed diagnosis method based on graphic recognition technology, experimental study is carried out on 600MW supercritical modeling turbine rotor bearing system for test-bed, simulating normal state and different faults such as rotor’s unbalance, rotors’misalignment, steam-excited vibration, bear’s looseness, dynamic static rub and impact ,and so on. Relatively higher accuracy is obtained, when diagnosing faults with diagnosis method based on graphic recognition which is proposed in the article.
引文
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