基于局域波时频谱的往复机故障智能诊断方法研究
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摘要
故障诊断对于机械设备的重要性是不言而喻的。由于往复式压缩机在工业生产的特殊地位,针对其故障诊断方法的研究显得尤其重要。类同机电设备的主要诊断过程,往复式压缩机组的故障智能诊断主要分为三部分:第一是诊断信息的获取;第二是故障特征的提取;第三是故障源和故障类别的最终确定。考虑到这一思想,基于国家自然科学基金“局域波法及其工程应用研究”(No.50475155),在总结和汲取前人研究成果的基础上,对局域波法中的相关理论和方法进行改进,随后结合局域波理论、模糊二元树诊断、系统级故障诊断和灰色预测等方法及理论针对往复式压缩机组系统故障智能诊断方法进行了深入的研究,并将该方法在工程应用上予以验证。主要的工作如下:
1.针对一维局域波理论的待改进点和二维局域波在图像诊断中的应用进行了深入的研究。在阐述局域波分解算法和时频分析理论的基础上,分析了一维局域波理论中存在的待改进点,包括微弱信号对强信号的影响和局域波时频谱处理等方面的应用研究。对适合于局域波时频谱特征提取的三种故障分类器的分类特性进行了对比分析研究。提出了基于二维局域波分解的图像诊断方法,通过对时频灰度图像的二维分解提取表征故障信息的图像细节部分,有效地实现了故障的特征提取。结合故障分类器运用局域波时频分析方法,对往复压缩机气阀表面振动信号进行综合分析,有效地识别了故障,体现出该方法对于分析往复机械的振动特点,在故障信息提取中效果较为理想。
2.分析了往复式压缩机结构及工作原理并阐述了往复式压缩机振动的主要激励源,研究了往复式压缩机的主要振动形式和振动信号的特征,随后分析并研究了测点位置的优化布置方法,最终实现了往复机组的在线监测。
3.提出了基于局域波时频图的故障模糊二元树诊断方法。对往复压缩机振动信号进行局域波时频处理,得到包含故障状态值的局域波时频谱,结合系统工况资料建立系统故障状态一特征表,找出最大故障信息量的特征群,建成故障信息量的模糊二元树。由模糊二元树分析故障,找出使路径信息量最大的部件,即为故障可能性最大的部件。即由模糊图来实现故障点的凸显过程。
4.提出了基于局域波时频图的系统级故障诊断方法。大型往复式压缩机组结构的复杂性和子系统故障特征的多样性决定了其故障的难确定性。这里引入系统级故障诊断理论,分析了系统级故障诊断应用于往复式压缩机故障诊断的可行性问题,通过改进PMC测试模型集团算法的应用,提出了一种基于局域波时频谱的系统级故障诊断方法。
5.通过分析往复式压缩机组系统的特点,引入灰色预测模型,从非线性时间序列预测的角度,对系统的状态预测进行了研究。首先对复杂过程工业系统进行灰色预测的可行性进行了分析。以振动信号能量谱作为输入量,选择SCGM预测模型,建立了系统状态的预测模型。该方法给出了系统状态预测的新思路,并通过实例对往复式压缩机系统行了短期预测,结果说明基于局域波和灰色预测模型的故障预测方法有较高的预测精度,适合于往复式压缩机组的故障状态波动的预测。
It is self-evident that fault diagnosis is necessary for mechanical equipments, especially for reciprocating machines that play an important role in industries. Similar to the main process of ordinary equipments, fault diagnosis can be divided into three parts: achieving the message for diagnosis, extracting the diagnostic feature and assuring the fault node & classifying the fault. Above this basic idea, the research is carried out based on the "Research on Local Wave Method and its Engineering Application" (supported by Chinese National Nature Science foundation, Project No.50475155) and former correlative research, in which the reciprocating machine is set as the target. The research includes improving certain algorithm of Local Wave Method, the combination of Local Wave Method and fuzzy duality tree theory, Local Wave Method and system-level fault diagnosis model, and Local Wave Method and SCGM predicting model, while the methods are testified in real application. The main work of the dissertation is list as follows:
1. Aiming at the defects of Local Wave Method and the main application of Bidimensional Local Wave Method in image analysis, the related research is processed. Based on the basic theory of Local Wave Method, the existing defects is analyzed firstly, including the influence imposed on strong signal by weak, the application method of Local Wave time-frequency spectrum, and etc. Three main types of classification used in attracting fault feature from Local Wave time-frequency spectrum are contrasted. The image diagnosis method is presented based on bidimensional Local Wave Method. The grey image of time-frequency spectrum is set as the study target, from which the character correlative to fault is attracted. The method is summarized that the analyzing method involving with Local Wave time-frequency spectrum is effective in identifying the fault from the vibration signals obtained from the reciprocating compressor surface, while the fault degree is confirmed. Therefore, the method is perfect for fault diagnosis of reciprocating compressor because of its utility in analyzing the special vibration character.
2. The mechanism of the reciprocating is analyzed. On discussing the main fault source in the construction, the main vibration styles and the main character of the vibration signal are studied. Then the topology of the sampling position distribution is optimized. Finally, the importance of the containing character of the signal in on-line controlling and according fault diagnosis is analyzed.
3. The diagnosis method based on fuzzy duality tree and the local wave time-frequency spectrum is presented. The spectrum is achieved from the locale vibration signal applying Local Wave Method, on which the state-feature table of the system is established. Then the feature group with the maximum of fault information is worked out, the fuzzy tree is established sequently. Applying the fuzzy duality tree, the component of possible fault which contributes the maximal information increment in transform paths is picked out. In this way, the fault source is protruded visually with the fuzzy tree.
4. The special system-level diagnosis method aiming at reciprocating machine is presented based on Local Wave Method. Confirming the fault of the reciprocating machine is difficult because of the complexity of the construction and the variety of the fault feature. In the dissertation, the system-level diagnosis is proposed. Firstly, the basic theory and algorithm of the system-level is discussed; secondly, the feasibility is studied, and lastly, with the special improved PMC model applied, the fault diagnosis method is presented and testified in project.
5. In view of the special character of the reciprocating machine, the compatible grey prediction model is proposed, with which, the system state is predicted in the field of non-linear time serial. The feasibility is studied firstly. Setting the energy spectrum of the vibration signal as the input, the compatible prediction model of SCGM is chosen in system prediction model establishing. The method provides a new orientation in system state prediction which is also testified in elementary prediction on the reciprocating machine The result illustrates that the presented model is effective in the state fluctuate prediction of reciprocating machine with great precision.
1.针对一维局域波理论的待改进点和二维局域波在图像诊断中的应用进行了深入的研究。在阐述局域波分解算法和时频分析理论的基础上,分析了一维局域波理论中存在的待改进点,包括微弱信号对强信号的影响和局域波时频谱处理等方面的应用研究。对适合于局域波时频谱特征提取的三种故障分类器的分类特性进行了对比分析研究。提出了基于二维局域波分解的图像诊断方法,通过对时频灰度图像的二维分解提取表征故障信息的图像细节部分,有效地实现了故障的特征提取。结合故障分类器运用局域波时频分析方法,对往复压缩机气阀表面振动信号进行综合分析,有效地识别了故障,体现出该方法对于分析往复机械的振动特点,在故障信息提取中效果较为理想。
2.分析了往复式压缩机结构及工作原理并阐述了往复式压缩机振动的主要激励源,研究了往复式压缩机的主要振动形式和振动信号的特征,随后分析并研究了测点位置的优化布置方法,最终实现了往复机组的在线监测。
3.提出了基于局域波时频图的故障模糊二元树诊断方法。对往复压缩机振动信号进行局域波时频处理,得到包含故障状态值的局域波时频谱,结合系统工况资料建立系统故障状态一特征表,找出最大故障信息量的特征群,建成故障信息量的模糊二元树。由模糊二元树分析故障,找出使路径信息量最大的部件,即为故障可能性最大的部件。即由模糊图来实现故障点的凸显过程。
4.提出了基于局域波时频图的系统级故障诊断方法。大型往复式压缩机组结构的复杂性和子系统故障特征的多样性决定了其故障的难确定性。这里引入系统级故障诊断理论,分析了系统级故障诊断应用于往复式压缩机故障诊断的可行性问题,通过改进PMC测试模型集团算法的应用,提出了一种基于局域波时频谱的系统级故障诊断方法。
5.通过分析往复式压缩机组系统的特点,引入灰色预测模型,从非线性时间序列预测的角度,对系统的状态预测进行了研究。首先对复杂过程工业系统进行灰色预测的可行性进行了分析。以振动信号能量谱作为输入量,选择SCGM预测模型,建立了系统状态的预测模型。该方法给出了系统状态预测的新思路,并通过实例对往复式压缩机系统行了短期预测,结果说明基于局域波和灰色预测模型的故障预测方法有较高的预测精度,适合于往复式压缩机组的故障状态波动的预测。
It is self-evident that fault diagnosis is necessary for mechanical equipments, especially for reciprocating machines that play an important role in industries. Similar to the main process of ordinary equipments, fault diagnosis can be divided into three parts: achieving the message for diagnosis, extracting the diagnostic feature and assuring the fault node & classifying the fault. Above this basic idea, the research is carried out based on the "Research on Local Wave Method and its Engineering Application" (supported by Chinese National Nature Science foundation, Project No.50475155) and former correlative research, in which the reciprocating machine is set as the target. The research includes improving certain algorithm of Local Wave Method, the combination of Local Wave Method and fuzzy duality tree theory, Local Wave Method and system-level fault diagnosis model, and Local Wave Method and SCGM predicting model, while the methods are testified in real application. The main work of the dissertation is list as follows:
1. Aiming at the defects of Local Wave Method and the main application of Bidimensional Local Wave Method in image analysis, the related research is processed. Based on the basic theory of Local Wave Method, the existing defects is analyzed firstly, including the influence imposed on strong signal by weak, the application method of Local Wave time-frequency spectrum, and etc. Three main types of classification used in attracting fault feature from Local Wave time-frequency spectrum are contrasted. The image diagnosis method is presented based on bidimensional Local Wave Method. The grey image of time-frequency spectrum is set as the study target, from which the character correlative to fault is attracted. The method is summarized that the analyzing method involving with Local Wave time-frequency spectrum is effective in identifying the fault from the vibration signals obtained from the reciprocating compressor surface, while the fault degree is confirmed. Therefore, the method is perfect for fault diagnosis of reciprocating compressor because of its utility in analyzing the special vibration character.
2. The mechanism of the reciprocating is analyzed. On discussing the main fault source in the construction, the main vibration styles and the main character of the vibration signal are studied. Then the topology of the sampling position distribution is optimized. Finally, the importance of the containing character of the signal in on-line controlling and according fault diagnosis is analyzed.
3. The diagnosis method based on fuzzy duality tree and the local wave time-frequency spectrum is presented. The spectrum is achieved from the locale vibration signal applying Local Wave Method, on which the state-feature table of the system is established. Then the feature group with the maximum of fault information is worked out, the fuzzy tree is established sequently. Applying the fuzzy duality tree, the component of possible fault which contributes the maximal information increment in transform paths is picked out. In this way, the fault source is protruded visually with the fuzzy tree.
4. The special system-level diagnosis method aiming at reciprocating machine is presented based on Local Wave Method. Confirming the fault of the reciprocating machine is difficult because of the complexity of the construction and the variety of the fault feature. In the dissertation, the system-level diagnosis is proposed. Firstly, the basic theory and algorithm of the system-level is discussed; secondly, the feasibility is studied, and lastly, with the special improved PMC model applied, the fault diagnosis method is presented and testified in project.
5. In view of the special character of the reciprocating machine, the compatible grey prediction model is proposed, with which, the system state is predicted in the field of non-linear time serial. The feasibility is studied firstly. Setting the energy spectrum of the vibration signal as the input, the compatible prediction model of SCGM is chosen in system prediction model establishing. The method provides a new orientation in system state prediction which is also testified in elementary prediction on the reciprocating machine The result illustrates that the presented model is effective in the state fluctuate prediction of reciprocating machine with great precision.
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