大型汽轮发电机组故障诊断方法及监测保护系统研究
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摘要
汽轮发电机组是火电厂关键设备,一旦发生故障,将造成非常大的经济损失和不利影响。随着机组朝大型化和高参数方向发展,单台机组投资规模和影响也相应增大,对于机组运行安全可靠性的要求不断提高。深入开展大型汽轮发电机组状态监测与故障诊断新方法与新技术的研究,对于保障这类大型复杂设备的安全可靠运行具有重要意义。
大型汽轮发电机结构复杂,而且机、电、液耦合,故障信号具有背景噪声干扰大、非平稳、非线性的特点,其传播过程途径与衰减特性复杂,往往是多故障源信号混叠在一起,对故障信息的正确分析与获取,进而准确地诊断故障造成困难。因此,研究故障信号的特征分析与提取技术,从监测的信号中获取正确的故障特征信息,是进行准确故障诊断的技术关键。本文正是在这样的背景下,结合国家863课题“超临界、超超临界大型汽轮发电机组状态监测与故障诊断技术及其系统研究(2008AA042410)”,开展大型汽轮发电机组在复杂运行环境下振动信号监测与新的故障特征提取技术的研究。主要研究内容包括三个部分:一是基于独立分量分析的故障源分离新方法;二是基于高阶统计分析的故障特征提取新方法;三是汽轮机监测保护与故障诊断系统的研发。各个部分的具体研究内容和主要成果如下:
1)研究复杂运行环境下基于独立分量分析(Independent Component Analysis—ICA)的故障源分离及故障信息提取技术,并利用实际汽轮机转子系统振动信号进行验证,实现从监测的信号中获取准确充分的故障特征信息。分析了以不同测量通道数量、不同振动信号类型组合和不同测点位置组成输入信号的ICA分离效果,并对机组在故障状态下,从测量信号中分离出故障源的可能性进行了探讨,为ICA在汽轮机振动故障源分离方面的应用提供丰富的分析实例。
2)针对超完备基ICA的工程应用问题,提出附加虚拟通道ICA的新方法,利用某些已经具有先验知识的独立分量构成附加输入信号(称为附加虚拟通道),与其它测量信号组合在一起进行ICA分离,达到增加输入信号数量的目的。首先研究了附加虚拟通道ICA的模型,并对分离效果进行了仿真验证。进一步将附加虚拟通道ICA方法应用到汽轮机转子系统振动源分离问题,特别是对在故障源分离方面更具有实际意义的“延时时刻虚拟通道ICA”问题进行了实例验证,即利用某个时刻测量数据分离得到的独立分量作为虚拟通道,去分离未来时刻的独立分量。结果表明虚拟通道ICA对于延时时刻的ICA分离问题同样具有明显的效果。这样就可以使用较少数量的传感器测量信号实现对故障源的分离,为超完备基ICA问题的工程应用提供了很好的解决方法。
3)认为卷积性混合ICA模型更适合描述汽轮机转子系统振动源分离问题,并提出用傅立叶变换解决这类具有卷积性混合ICA模型的工程实际的源分离问题。即通过对模型求傅立叶变换,将卷积混合关系转化为线性混合关系,利用线性ICA的计算方法实现独立分量的快速分离。应用实际汽轮机振动测量信号对该方法进行的全面分析验证,结果表明,频域ICA可以分离出轴振测量信号中包含的可能很微弱的故障信息,分离结果清晰,比基本ICA方法具有明显的优势。
4)遵循理论与实践相结合原则,通过一系列仿真实验有针对性的验证、探究了高阶谱的本质,并利用高阶累积量的双谱、双相干谱、1(1/2)维谱等高阶统计分析方法,对汽轮机异常振动信号进行高阶统计特性分析,提取故障特征值。
5)对汽轮机发电机组监测保护系统(TSI)的可靠性设计技术进行攻关,自主研制了具有保护功能的在线状态监测系统和远程化智能化故障诊断系统,并应用于工程实际;对所研究方法、技术和系统进行实验测试,进一步完善和提高方法、技术及系统的可靠性。
Turbine generator set is regarded as key equipments for the coal fire power station. It will sometime lead to enormous economically and financial losses and creating negative effects once the turbine generator set is out of control.With regard to the turbine generator set to be well developed toward creating a large scale and high parameters orientation,the scale and influencing impacts created by utilizing uni-generator set have been accordingly enhanced.This has increased greatly demands on the operational reliability for the exploration of turbine generator.It is believed to launch large scale of monitoring on large scale of turbine generator set,failure diagnosis method and new technology research,which exercise significant impacts on maintaining the operational quality and the safety for the sophistical turbine generator equipment.
The structure of a large turbine generator tends to be complicated with the combination of electro-mechanical and electro-hydraulic.The failure signal is with background noise interfaces,nonlinear and nonstationary character.The rote of its transformation process is in combination with its natured decreasing sophistication often mixed with failure signal source together.This has created great difficulties for the correct diagnosis failure signal,feature study and capturing its technology.Obtaining information from correct failure signal is the critical technology for the failure signal diagnosis.The dissertation is undertaken by consideration the above explanations, together with the national 863 program and it aims at 'supercritical,ultra supercritical turbine generator monitoring,failure diagnosis technology and system research (2008AA04Z410)".Launching large turbine generator set research under the complicated background for the vibration signal monitoring,the primary research of the dissertation include three parts:The first part is to diversify new method from an independent component analysis;The second part is to generate new method based upon high-order statistical analysis;and the third part is to research on the monitoring and protection to the turbine generator.The main achievements and precise contents of the above three primary parts are summarized as the follows.
1.With regard to the sophisticated operational environment,research on the failure source separation system and failure obtaining technology undertaken by using independent component analysis-ICA.The research can take advantage of turbine rotation system and vibration signal to testify and it can realize correct and sufficient information from the monitoring signal and the failure feature.The dissertation analysis different measurement on channel frequency,different vibration signals sets and different location to form each ICA result.With regard to the failure diagnosis of the turbine generator operation,the study has explored the possibilities to detect the failure diagnosis source from the measurement signals.It has also provided sufficient analysis case for the application of separation to detecting failure source on the turbine generator.
2.Aiming at the engineering application of overcomplete bases ICA,the dissertation has raised virtual channel on the new method of ICA.It take advantage of some testified knowledge to the constitution of ICA known as(addition virtual channel), together with other measurement signal to proceed ICA separation,which can increase frequency on the input signal.Research firstly should be undertaken additional virtual channel model,and it can further testify the separation effects by using simulation signal. The additional virtual channel to the application of ICA method can be applied to turbine rotation system for system vibration.Especially it exercises great and actual impacts to the failure source separation and it can further testify the research on 'delay virtual channel ICA'.It means to take advantage of a specific moment to data on diagnosing independent component as virtual channel and the separation of ICA at future timing.In conducting such way,it can use few sensors measurement signal to realize the separation of the faulty source and it can also provide many good solutions for the application of ICA.
3.In the consideration of convolution mixing ICA model for the best fitting the turbine rotation system and separation issue,the dissertation raised a kind of analysis on the fourier transform model,it can transfer from rotation mix to linear mix relation. Hence it can take advantage of ICA calculation method to separate vastly from the ICA analysis.The research can make use of actual turbine measurement signal to testify the above method.The result of conducting such research demonstrates,frequency range of ICA can detect possible some tiny and weaken failure information.The result of the separation effects is clear and it has some obvious advantages in comparison with ICA method.
4.Following the principle of the theory in combination with practices,with a series of objective rectification and research on the high-order spectrum natured analysis,this research has explored advance level statistical analysis to obtain the faulty feature variables and accumulated bispectrum and bicoherence spectrum analysis,1(1/2) dimension spectrum analysis including un normal vibration signal of turbine generator, gear cracking fault vibration signal point.
5.With the consideration of the foundation on detecting normal vibration signal monitoring technology,the study researches mostly on turbine generator set monitoring system and its reliability design technology.The research is mainly embedded system in operation protection function for on line monitoring system.The research includes building up typical faulty database for the turbine generator,researching intelligence faulty diagnosis system,developing multi physics amount and long range faulty diagnosis system.The research aims at upgrading method,technology and system with further rectification and this is believed to enhance the diagnosis method,technology and reliabilities.
大型汽轮发电机结构复杂,而且机、电、液耦合,故障信号具有背景噪声干扰大、非平稳、非线性的特点,其传播过程途径与衰减特性复杂,往往是多故障源信号混叠在一起,对故障信息的正确分析与获取,进而准确地诊断故障造成困难。因此,研究故障信号的特征分析与提取技术,从监测的信号中获取正确的故障特征信息,是进行准确故障诊断的技术关键。本文正是在这样的背景下,结合国家863课题“超临界、超超临界大型汽轮发电机组状态监测与故障诊断技术及其系统研究(2008AA042410)”,开展大型汽轮发电机组在复杂运行环境下振动信号监测与新的故障特征提取技术的研究。主要研究内容包括三个部分:一是基于独立分量分析的故障源分离新方法;二是基于高阶统计分析的故障特征提取新方法;三是汽轮机监测保护与故障诊断系统的研发。各个部分的具体研究内容和主要成果如下:
1)研究复杂运行环境下基于独立分量分析(Independent Component Analysis—ICA)的故障源分离及故障信息提取技术,并利用实际汽轮机转子系统振动信号进行验证,实现从监测的信号中获取准确充分的故障特征信息。分析了以不同测量通道数量、不同振动信号类型组合和不同测点位置组成输入信号的ICA分离效果,并对机组在故障状态下,从测量信号中分离出故障源的可能性进行了探讨,为ICA在汽轮机振动故障源分离方面的应用提供丰富的分析实例。
2)针对超完备基ICA的工程应用问题,提出附加虚拟通道ICA的新方法,利用某些已经具有先验知识的独立分量构成附加输入信号(称为附加虚拟通道),与其它测量信号组合在一起进行ICA分离,达到增加输入信号数量的目的。首先研究了附加虚拟通道ICA的模型,并对分离效果进行了仿真验证。进一步将附加虚拟通道ICA方法应用到汽轮机转子系统振动源分离问题,特别是对在故障源分离方面更具有实际意义的“延时时刻虚拟通道ICA”问题进行了实例验证,即利用某个时刻测量数据分离得到的独立分量作为虚拟通道,去分离未来时刻的独立分量。结果表明虚拟通道ICA对于延时时刻的ICA分离问题同样具有明显的效果。这样就可以使用较少数量的传感器测量信号实现对故障源的分离,为超完备基ICA问题的工程应用提供了很好的解决方法。
3)认为卷积性混合ICA模型更适合描述汽轮机转子系统振动源分离问题,并提出用傅立叶变换解决这类具有卷积性混合ICA模型的工程实际的源分离问题。即通过对模型求傅立叶变换,将卷积混合关系转化为线性混合关系,利用线性ICA的计算方法实现独立分量的快速分离。应用实际汽轮机振动测量信号对该方法进行的全面分析验证,结果表明,频域ICA可以分离出轴振测量信号中包含的可能很微弱的故障信息,分离结果清晰,比基本ICA方法具有明显的优势。
4)遵循理论与实践相结合原则,通过一系列仿真实验有针对性的验证、探究了高阶谱的本质,并利用高阶累积量的双谱、双相干谱、1(1/2)维谱等高阶统计分析方法,对汽轮机异常振动信号进行高阶统计特性分析,提取故障特征值。
5)对汽轮机发电机组监测保护系统(TSI)的可靠性设计技术进行攻关,自主研制了具有保护功能的在线状态监测系统和远程化智能化故障诊断系统,并应用于工程实际;对所研究方法、技术和系统进行实验测试,进一步完善和提高方法、技术及系统的可靠性。
Turbine generator set is regarded as key equipments for the coal fire power station. It will sometime lead to enormous economically and financial losses and creating negative effects once the turbine generator set is out of control.With regard to the turbine generator set to be well developed toward creating a large scale and high parameters orientation,the scale and influencing impacts created by utilizing uni-generator set have been accordingly enhanced.This has increased greatly demands on the operational reliability for the exploration of turbine generator.It is believed to launch large scale of monitoring on large scale of turbine generator set,failure diagnosis method and new technology research,which exercise significant impacts on maintaining the operational quality and the safety for the sophistical turbine generator equipment.
The structure of a large turbine generator tends to be complicated with the combination of electro-mechanical and electro-hydraulic.The failure signal is with background noise interfaces,nonlinear and nonstationary character.The rote of its transformation process is in combination with its natured decreasing sophistication often mixed with failure signal source together.This has created great difficulties for the correct diagnosis failure signal,feature study and capturing its technology.Obtaining information from correct failure signal is the critical technology for the failure signal diagnosis.The dissertation is undertaken by consideration the above explanations, together with the national 863 program and it aims at 'supercritical,ultra supercritical turbine generator monitoring,failure diagnosis technology and system research (2008AA04Z410)".Launching large turbine generator set research under the complicated background for the vibration signal monitoring,the primary research of the dissertation include three parts:The first part is to diversify new method from an independent component analysis;The second part is to generate new method based upon high-order statistical analysis;and the third part is to research on the monitoring and protection to the turbine generator.The main achievements and precise contents of the above three primary parts are summarized as the follows.
1.With regard to the sophisticated operational environment,research on the failure source separation system and failure obtaining technology undertaken by using independent component analysis-ICA.The research can take advantage of turbine rotation system and vibration signal to testify and it can realize correct and sufficient information from the monitoring signal and the failure feature.The dissertation analysis different measurement on channel frequency,different vibration signals sets and different location to form each ICA result.With regard to the failure diagnosis of the turbine generator operation,the study has explored the possibilities to detect the failure diagnosis source from the measurement signals.It has also provided sufficient analysis case for the application of separation to detecting failure source on the turbine generator.
2.Aiming at the engineering application of overcomplete bases ICA,the dissertation has raised virtual channel on the new method of ICA.It take advantage of some testified knowledge to the constitution of ICA known as(addition virtual channel), together with other measurement signal to proceed ICA separation,which can increase frequency on the input signal.Research firstly should be undertaken additional virtual channel model,and it can further testify the separation effects by using simulation signal. The additional virtual channel to the application of ICA method can be applied to turbine rotation system for system vibration.Especially it exercises great and actual impacts to the failure source separation and it can further testify the research on 'delay virtual channel ICA'.It means to take advantage of a specific moment to data on diagnosing independent component as virtual channel and the separation of ICA at future timing.In conducting such way,it can use few sensors measurement signal to realize the separation of the faulty source and it can also provide many good solutions for the application of ICA.
3.In the consideration of convolution mixing ICA model for the best fitting the turbine rotation system and separation issue,the dissertation raised a kind of analysis on the fourier transform model,it can transfer from rotation mix to linear mix relation. Hence it can take advantage of ICA calculation method to separate vastly from the ICA analysis.The research can make use of actual turbine measurement signal to testify the above method.The result of conducting such research demonstrates,frequency range of ICA can detect possible some tiny and weaken failure information.The result of the separation effects is clear and it has some obvious advantages in comparison with ICA method.
4.Following the principle of the theory in combination with practices,with a series of objective rectification and research on the high-order spectrum natured analysis,this research has explored advance level statistical analysis to obtain the faulty feature variables and accumulated bispectrum and bicoherence spectrum analysis,1(1/2) dimension spectrum analysis including un normal vibration signal of turbine generator, gear cracking fault vibration signal point.
5.With the consideration of the foundation on detecting normal vibration signal monitoring technology,the study researches mostly on turbine generator set monitoring system and its reliability design technology.The research is mainly embedded system in operation protection function for on line monitoring system.The research includes building up typical faulty database for the turbine generator,researching intelligence faulty diagnosis system,developing multi physics amount and long range faulty diagnosis system.The research aims at upgrading method,technology and system with further rectification and this is believed to enhance the diagnosis method,technology and reliabilities.
引文
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