离心压缩机组振动智能诊断关键技术研究
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摘要
论文主要对离心压缩机组振动故障自动监测与智能诊断方法进行了系统的研究,分别对信号特征自动提取、异常检测方法、诊断规则自动提取、智能诊断方法等方面进行了研究。在此基础上,建立了离心压缩机组智能诊断系统,并通过实际案例检验了所研究方法和诊断系统的有效性。
论文第二章研究了基于现代信号分析方法的信号特征自动提取方法,重点对振动信号特征提取中的轴心轨迹自动识别方法进行了研究,提出了基于图像处理方法和小波神经网络的轴心轨迹自动识别方法,同时利用Hilbert-Huang变换方法对弱振动信号特征提取方法进行了研究,取得了很好的效果。
论文第三章针对大型离心压缩机组故障类型和故障征兆之间不是一一对应的,存在着非线性映射特征。而现有的故障诊断方法中,难于满足大型离心压缩机组的动态故障诊断和对于故障的智能化诊断的需要问题,研究了基于神经网络的智能诊断方法。通过分析神经网络对离心压缩机组故障分类的有效性,为了提高故障诊断的有效性和准确性,提出了基于自组织神经网络的故障自动分类方法。通过对实际信号的分析,验证了方法的有效性。
论文第四章在离心压缩机组异常检测这个故障诊断的重要内容进行了研究,针对压缩机组振动信号异常状态的复杂性,提出了把改进型反面选择算法应用于压缩机组振动故障的检测,,建立了压缩机组振动异常状态检测器,实现了高效、快速的压缩机组振动异常检测,并通过实际案例进行了验证。
论文第五章针对压缩机组智能诊断中的信息冗余问题,提出了一种基于粗糙集的知识约简方法,通过对实际案例的应用,证明了该方法可以大幅度的简化诊断的知识结构,大大提高了诊断效率。
论文第六章综合前几章所研究的方法,建立了离心压缩机组振动智能诊断系统,并应用于现场压缩机组故障诊断中。该章主要研究了压缩机组智能诊断系统的结构框架设计,压缩机组智能诊断专家系统的实现等方面的问题,该章最后通过实际的诊断案例分析,证明了利用人工智能诊断方法是实现压缩机组智能诊断的有效途径。
本论文对离心压缩机组智能诊断技术进行了系统研究,对于各章所提出的方法,在每章最后均利用仿真和实际信号进行了验证,而在最后一章,更是通过多个现场实际案例对智能诊断系统进行了验证。
The present dissertation focuses on the study of centrifugal compressor fault vibrationautonomous detection and diagnosis method.The main content is about the signalcharacteristic identification、abnormity detection、diagnosis rule automatic abstract、diagnosis method etc. On the basis, the centrifugal compressor fault diagnosis system wasbuilt up. The usefulness of research method and the diagnosis system was examinedpractically.
The chapter 2 is about the automatic identification of signal characteristic usingmodern signal processing method. It includes the identification of the orbital's featureusing image processing and Wavelet NN and the identification of small vibration signalfeature using Hilbert-Huang Transform.
The chapter 3 is about the problem that the fault model of rotation machine and thefeature of the fault is not one to one. It exists the characteristic of non-linear reflectionbetween the fault model of rotation machine and the feature of the fault. But the existingmethod of fault diagnosis is difficult to satisfy the need of the dynamic fault diagnosis forrotational machine. For this purpose, we established a classified method of rotationalmachine based on Kohonen neural network for the fault classification. It was checked bycompressor vibration signals.
The fault detection approaches of centrifugal compressors have been investigated inchapter 4. As to the vibration signals of centrifugal compressors in abnormal condition arevery complex, fault detection approach based on immune mechanisms is proposed. Thekey techniques of proposed fault detection approach, such as description of equipmentcondition, matching function of detectors, algorithm to generate detectors and optimizationof detectors are lucubrated. The effectiveness of the method is demonstrated by some faultcases of the test bed.
In chapter 5 ,on the problem of redundancy information in compressor intelligencediagnosis, it puts forward a knowledge reduction arithmetic based on rough sets .And it was proved that the knowledge architecture for diagnosis can be reduced greatly bythe arithmetic.
In chapter 6, under the synthesis of the method mentioned before, the vibrationintelligence diagnosis system of centrifugal compressor was built up .The system hasbeen used practically. In this chapter the architecture of the diagnosis system and thediagnosis expert code have been studied. Finally, through the analysis of examples,theresult shows that the intelligent diagnosis method is suitable for centrifugal compressorvibration fault diagnosis.
In this dissertation, several intelligent diagnosis method of centrifugal compressor havebeen studied. The presented methods in every chapter have been checked by simulationand real signal. At the last chapter, the effect of the diagnosis system has been checkedthrough several examples.
论文第二章研究了基于现代信号分析方法的信号特征自动提取方法,重点对振动信号特征提取中的轴心轨迹自动识别方法进行了研究,提出了基于图像处理方法和小波神经网络的轴心轨迹自动识别方法,同时利用Hilbert-Huang变换方法对弱振动信号特征提取方法进行了研究,取得了很好的效果。
论文第三章针对大型离心压缩机组故障类型和故障征兆之间不是一一对应的,存在着非线性映射特征。而现有的故障诊断方法中,难于满足大型离心压缩机组的动态故障诊断和对于故障的智能化诊断的需要问题,研究了基于神经网络的智能诊断方法。通过分析神经网络对离心压缩机组故障分类的有效性,为了提高故障诊断的有效性和准确性,提出了基于自组织神经网络的故障自动分类方法。通过对实际信号的分析,验证了方法的有效性。
论文第四章在离心压缩机组异常检测这个故障诊断的重要内容进行了研究,针对压缩机组振动信号异常状态的复杂性,提出了把改进型反面选择算法应用于压缩机组振动故障的检测,,建立了压缩机组振动异常状态检测器,实现了高效、快速的压缩机组振动异常检测,并通过实际案例进行了验证。
论文第五章针对压缩机组智能诊断中的信息冗余问题,提出了一种基于粗糙集的知识约简方法,通过对实际案例的应用,证明了该方法可以大幅度的简化诊断的知识结构,大大提高了诊断效率。
论文第六章综合前几章所研究的方法,建立了离心压缩机组振动智能诊断系统,并应用于现场压缩机组故障诊断中。该章主要研究了压缩机组智能诊断系统的结构框架设计,压缩机组智能诊断专家系统的实现等方面的问题,该章最后通过实际的诊断案例分析,证明了利用人工智能诊断方法是实现压缩机组智能诊断的有效途径。
本论文对离心压缩机组智能诊断技术进行了系统研究,对于各章所提出的方法,在每章最后均利用仿真和实际信号进行了验证,而在最后一章,更是通过多个现场实际案例对智能诊断系统进行了验证。
The present dissertation focuses on the study of centrifugal compressor fault vibrationautonomous detection and diagnosis method.The main content is about the signalcharacteristic identification、abnormity detection、diagnosis rule automatic abstract、diagnosis method etc. On the basis, the centrifugal compressor fault diagnosis system wasbuilt up. The usefulness of research method and the diagnosis system was examinedpractically.
The chapter 2 is about the automatic identification of signal characteristic usingmodern signal processing method. It includes the identification of the orbital's featureusing image processing and Wavelet NN and the identification of small vibration signalfeature using Hilbert-Huang Transform.
The chapter 3 is about the problem that the fault model of rotation machine and thefeature of the fault is not one to one. It exists the characteristic of non-linear reflectionbetween the fault model of rotation machine and the feature of the fault. But the existingmethod of fault diagnosis is difficult to satisfy the need of the dynamic fault diagnosis forrotational machine. For this purpose, we established a classified method of rotationalmachine based on Kohonen neural network for the fault classification. It was checked bycompressor vibration signals.
The fault detection approaches of centrifugal compressors have been investigated inchapter 4. As to the vibration signals of centrifugal compressors in abnormal condition arevery complex, fault detection approach based on immune mechanisms is proposed. Thekey techniques of proposed fault detection approach, such as description of equipmentcondition, matching function of detectors, algorithm to generate detectors and optimizationof detectors are lucubrated. The effectiveness of the method is demonstrated by some faultcases of the test bed.
In chapter 5 ,on the problem of redundancy information in compressor intelligencediagnosis, it puts forward a knowledge reduction arithmetic based on rough sets .And it was proved that the knowledge architecture for diagnosis can be reduced greatly bythe arithmetic.
In chapter 6, under the synthesis of the method mentioned before, the vibrationintelligence diagnosis system of centrifugal compressor was built up .The system hasbeen used practically. In this chapter the architecture of the diagnosis system and thediagnosis expert code have been studied. Finally, through the analysis of examples,theresult shows that the intelligent diagnosis method is suitable for centrifugal compressorvibration fault diagnosis.
In this dissertation, several intelligent diagnosis method of centrifugal compressor havebeen studied. The presented methods in every chapter have been checked by simulationand real signal. At the last chapter, the effect of the diagnosis system has been checkedthrough several examples.
引文
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