现代航空燃气涡轮发动机故障分析与智能诊断关键技术研究
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摘要
以军用飞机和导弹为代表的现代武器装备的动力装置——航空燃气涡轮发动机是一个典型的复杂机械系统,其结构复杂,工作状况恶劣,容易发生各种机械故障。发动机的气路部件的失效、旋转部件的振动和摩擦副的磨损等各类故障均严重影响其运行的安全性、可靠性和高效性。因此,提高和完善发动机的状态监测和故障诊断技术,为涡轮喷气武器装备的研制提供有力的技术支撑,具有十分重要的意义。
对燃气涡轮发动机的故障研究与分析可以分为两方面问题:其一是对故障发动机进行分解拆卸,对故障进行断口及裂纹分析及寿命评估,发现其故障原因和机理,从而发现设计的薄弱环节并改进之。其中故障模式影响分析是实施发动机故障分析的重要方法和手段;其二,在不进行发动机分解的情况下,仅仅通过检测故障发动机的有限检测参数来实现故障定位、定性及定因。燃气涡轮发动机由于存在强烈非线性、非平稳性、不确定性等复杂系统特征,导致了基于传统经典数学理论建模求解的困难性,因此基于多源、异构、不完备、不确定信息的复杂系统的状态诊断问题变得极富挑战性。而神经网络、遗传算法、模糊逻辑、专家系统及粗糙集理论等非经典数学方法为解决此类问题提供了有效途径。
本文围绕现代燃气涡轮发动机的故障分析与智能诊断若干关键问题展开了研究,现将文中的主要工作内容及创新点总结如下:
(1)军用发动机故障模式影响分析(Fault Mode Effect Analysis:FMEA)是发动机研制过程中可靠性工程的重要部分,通过开展FMEA工作,对发动机所有可能潜在的故障模式作出分析并研究故障产生的原因,能够发现设计上的可靠性薄弱环节,相应提出预防和解决的对策,最终能够提高军用发动机的可靠性,并确保发动机在飞行试验中可靠工作。本章针对某军用发动机试样研制阶段的地面、飞行试验各阶段中可能发生的各种故障模式、发生故障的原因及其对发动机工作过程和飞行试验的影响进行了分析,列出了该型发动机试样研制阶段的FMEA表。
(2)利用军用发动机故障模式影响分析(FMEA)方法,对发动机高压涡轮叶片可能潜在的断裂故障模式做出FMEA分析。在发动机残骸分解检查、高压涡轮转子叶片断口分析、发动机性能计算、控制系统工作分析、发动机工作寿命分析等工作的基础上,对故障发生的原因和机理进行了研究,形成飞行试验中某发动机高压涡轮叶片断裂故障发生的原因及结论。
(3)在燃汽涡轮故障诊断中,应该充分利用各种信息,而不应仅局限于一种信息。因为从诊断学角度来看,任何一种诊断信息都是模糊的不精确的。任何一种诊断对象,单用一方面信息来反映其状态行为都是不完整的,只有从多方面获得关于同一对象的多维信息,并加以融合利用,才能对发动机进行更可靠、更准确的监测与诊断。本文提出一种集成神经网络的融合诊断方法,并针对燃气涡轮发动机磨损故障诊断问题,实现发动机磨损故障的融合诊断。
(4)基于规则的专家系统是人工智能应用最为成功的领域。本文将基于知识规则的诊断方法应用于燃气涡轮发动机的磨损故障诊断,构建了知识库、设计了正反向推理机、研究了基于粗糙集理论的知识自动获取方法,有效地克服了基于规则的专家系统知识获取困难的问题、同时针对实际情况中故障征兆的变化,提出了一种基于可扩展知识库的动态柔性诊断方法。
(5)由于案例的获取比规则容易,因此,基于案例推理(Case-Based Reasoning:CBR)方法是目前构建专家系统最有效的工具。本文研究了一种基于案例推理CBR的燃气涡轮发动机排故专家系统,首先构建了案例库;然后结合字符型字段匹配技术和KNN方法(最近相邻法)提出了针对发动机排故案例的案例检索模型;最后研究了基于差异驱动的案例修改方法。
(6)由于实际燃气涡轮发动机复杂系统的输入和传递特性往往无法获取,往往难于预测出发动机未来时刻的输出。此时,只能采用与系统分析相结合的时间序列分析法,将动态数据加以“系统”的处理而获得系统的数学模型。利用该模型来实现系统的辨识和对未来发展趋势的预测。本文运用神经网络预测方法,提出了基于结构自适应神经网络趋势预测方法,并运用于发动机性能趋势和磨损趋势预测。
Gas turbine aero-engine which is powerplant of modern weapons and equipments (materiel facility) such as avion and missile is a typical complex mechanical system. Its structure is complex, and its working condition is bad. And many kinds of mechanical faults happen easily for it. Many kinds of faults, such as invalidation of engine’s gas circuit parts, vibration of rotating parts, wear of friction pair, are all badly affect the security, reliability and high efficiency of its running, so it is very important and significative to improve and perfect condition monitoring and fault diagnosis technique of engine so as to offer technique support for the development of turbojet weapons and equipments.
The study and analysis on the faults of gas turbine aero-engine can be divided into two aspects: First, disassemble fault engine, analyze break and crack, evaluate life-span for faults in order to find fault causes and mechanism, thereby the weakness of design can be found and improved on. Where, the fault mode effect analysis (FMEA) is the important method and means for analyzing engine’s faults; second, under condition of engine not being disassembled, just check finite detection parameters of fault engine to localize fault, determine the nature and determine the causes. Because gas turbine some complex system characteristics of aero-engine, such as strong nonlinearity, imbalance and uncertainty, lead to the difficulty of modeling and solution based on traditional classical mathematic theories , so the condition diagnosis problems based on multi-source, isomerous, incomplete complex system with indefinite information become more challenging. Moreover, non-classical mathematic methods, such as neural network, genetic algorithm, fuzzy logic, expert system and rough set theories, offer effective approaches to solve these problems.
This paper has commenced the study on some pivotal problems about modern gas turbine engine’s fault analysis and intelligent diagnosis. Now the summary of main working contents and innovation points in this paper are as follows:
(1)The fault mode effect analysis (FMEA) of missile engine is the important part of reliability engineering in the development of engines. By developing the FMEA research, we can make analysis for all potential fault models of engine and study the causes of these faults, and can also find weakness of reliability in design and put forward the corresponding prevention and solution strategy. Finally, engine reliability can be improved and reliable operation of engine in the flight test can be insured. The paper mainly analyses many kinds of fault models happened possibly in the ground and flight test stages of some missile engine development, the causes of fault and the influence for the engine running and flight test, and lists the FMEA table of this type engine sample development stage.
(2)Using the FMEA method of missile engine, we can make FMEA analysis for the potential rupture fault mode of high pressure turbine blade. Based on the engine wreckage inspection after disassemble, the break analysis of high pressure turbine rotor blade, engine performance calculations, control system working analysis and engine life-span analysis etc., we have studied the causes and mechanism of fault, and get the conclusions and causes of some engine’s high pressure turbine blade break taking place in flight test.
(3)In the gas turbine fault diagnosis, we should make full use of much information, but not just pay attention to a sort of information. From the angle of diagnostics, any diagnosis information is ambiguous and inaccurate. And it is half-baked that any single information is to reflect the behavior of any kind of diagnostic object. We should get multidimensional information about the same object from many aspects, fuse and use them, so that we can carry out the more reliable and accurate monitoring and diagnosis for engine. The paper puts forward a kind of fusion diagnosis method based on integrated neural network, and realizes the engine wear fault fusion diagnosis aiming at the wear fault diagnosis problem of gas turbine engine.
(4)The expert system based on rules is the best successful field in artificial intelligence application. In this paper, we apply the diagnosis method based on knowledge rule into wear fault diagnosis of gas turbine engine, and build knowledge base, and design reversed reasoning machine, and study the method of knowledge acquisition automatically based on rough set theory. This method overcomes the problem that it is difficult to obtain knowledge in rule-based expert system effectively. At the same time, according to the change of fault symptom in practical case, we advance a dynamic flexible diagnosis method based on extensible knowledge base.
(5) Because case acquisition is easier than rule acquisition, so case-based reasoning (CBR) method is the most effective tool to build expert system. In this paper, we study a gas turbine engine troubleshooting expert system based on CBR. Firstly, we build case base; then combining character type fields matching technique with KNN method, advance case searching model aiming at engine troubleshooting cases; finally, we study the case modification method based on difference driving.
(6) Because the import and transfer characteristics of gas turbine engine complex system cannot be usually obtained, and it is difficult to forecast engine’s export in future. Then we can only use time series analysis method combining with system analysis, and use“system”to process dynamic data, and then obtain system mathematical model. And using this model to identify system and forecast the future development trend can come true. In this paper, using NN forecast method, we advance the trend forecast method based on structure-adaptive NN, and apply it into the trend forecasts of engine performance and wear.
对燃气涡轮发动机的故障研究与分析可以分为两方面问题:其一是对故障发动机进行分解拆卸,对故障进行断口及裂纹分析及寿命评估,发现其故障原因和机理,从而发现设计的薄弱环节并改进之。其中故障模式影响分析是实施发动机故障分析的重要方法和手段;其二,在不进行发动机分解的情况下,仅仅通过检测故障发动机的有限检测参数来实现故障定位、定性及定因。燃气涡轮发动机由于存在强烈非线性、非平稳性、不确定性等复杂系统特征,导致了基于传统经典数学理论建模求解的困难性,因此基于多源、异构、不完备、不确定信息的复杂系统的状态诊断问题变得极富挑战性。而神经网络、遗传算法、模糊逻辑、专家系统及粗糙集理论等非经典数学方法为解决此类问题提供了有效途径。
本文围绕现代燃气涡轮发动机的故障分析与智能诊断若干关键问题展开了研究,现将文中的主要工作内容及创新点总结如下:
(1)军用发动机故障模式影响分析(Fault Mode Effect Analysis:FMEA)是发动机研制过程中可靠性工程的重要部分,通过开展FMEA工作,对发动机所有可能潜在的故障模式作出分析并研究故障产生的原因,能够发现设计上的可靠性薄弱环节,相应提出预防和解决的对策,最终能够提高军用发动机的可靠性,并确保发动机在飞行试验中可靠工作。本章针对某军用发动机试样研制阶段的地面、飞行试验各阶段中可能发生的各种故障模式、发生故障的原因及其对发动机工作过程和飞行试验的影响进行了分析,列出了该型发动机试样研制阶段的FMEA表。
(2)利用军用发动机故障模式影响分析(FMEA)方法,对发动机高压涡轮叶片可能潜在的断裂故障模式做出FMEA分析。在发动机残骸分解检查、高压涡轮转子叶片断口分析、发动机性能计算、控制系统工作分析、发动机工作寿命分析等工作的基础上,对故障发生的原因和机理进行了研究,形成飞行试验中某发动机高压涡轮叶片断裂故障发生的原因及结论。
(3)在燃汽涡轮故障诊断中,应该充分利用各种信息,而不应仅局限于一种信息。因为从诊断学角度来看,任何一种诊断信息都是模糊的不精确的。任何一种诊断对象,单用一方面信息来反映其状态行为都是不完整的,只有从多方面获得关于同一对象的多维信息,并加以融合利用,才能对发动机进行更可靠、更准确的监测与诊断。本文提出一种集成神经网络的融合诊断方法,并针对燃气涡轮发动机磨损故障诊断问题,实现发动机磨损故障的融合诊断。
(4)基于规则的专家系统是人工智能应用最为成功的领域。本文将基于知识规则的诊断方法应用于燃气涡轮发动机的磨损故障诊断,构建了知识库、设计了正反向推理机、研究了基于粗糙集理论的知识自动获取方法,有效地克服了基于规则的专家系统知识获取困难的问题、同时针对实际情况中故障征兆的变化,提出了一种基于可扩展知识库的动态柔性诊断方法。
(5)由于案例的获取比规则容易,因此,基于案例推理(Case-Based Reasoning:CBR)方法是目前构建专家系统最有效的工具。本文研究了一种基于案例推理CBR的燃气涡轮发动机排故专家系统,首先构建了案例库;然后结合字符型字段匹配技术和KNN方法(最近相邻法)提出了针对发动机排故案例的案例检索模型;最后研究了基于差异驱动的案例修改方法。
(6)由于实际燃气涡轮发动机复杂系统的输入和传递特性往往无法获取,往往难于预测出发动机未来时刻的输出。此时,只能采用与系统分析相结合的时间序列分析法,将动态数据加以“系统”的处理而获得系统的数学模型。利用该模型来实现系统的辨识和对未来发展趋势的预测。本文运用神经网络预测方法,提出了基于结构自适应神经网络趋势预测方法,并运用于发动机性能趋势和磨损趋势预测。
Gas turbine aero-engine which is powerplant of modern weapons and equipments (materiel facility) such as avion and missile is a typical complex mechanical system. Its structure is complex, and its working condition is bad. And many kinds of mechanical faults happen easily for it. Many kinds of faults, such as invalidation of engine’s gas circuit parts, vibration of rotating parts, wear of friction pair, are all badly affect the security, reliability and high efficiency of its running, so it is very important and significative to improve and perfect condition monitoring and fault diagnosis technique of engine so as to offer technique support for the development of turbojet weapons and equipments.
The study and analysis on the faults of gas turbine aero-engine can be divided into two aspects: First, disassemble fault engine, analyze break and crack, evaluate life-span for faults in order to find fault causes and mechanism, thereby the weakness of design can be found and improved on. Where, the fault mode effect analysis (FMEA) is the important method and means for analyzing engine’s faults; second, under condition of engine not being disassembled, just check finite detection parameters of fault engine to localize fault, determine the nature and determine the causes. Because gas turbine some complex system characteristics of aero-engine, such as strong nonlinearity, imbalance and uncertainty, lead to the difficulty of modeling and solution based on traditional classical mathematic theories , so the condition diagnosis problems based on multi-source, isomerous, incomplete complex system with indefinite information become more challenging. Moreover, non-classical mathematic methods, such as neural network, genetic algorithm, fuzzy logic, expert system and rough set theories, offer effective approaches to solve these problems.
This paper has commenced the study on some pivotal problems about modern gas turbine engine’s fault analysis and intelligent diagnosis. Now the summary of main working contents and innovation points in this paper are as follows:
(1)The fault mode effect analysis (FMEA) of missile engine is the important part of reliability engineering in the development of engines. By developing the FMEA research, we can make analysis for all potential fault models of engine and study the causes of these faults, and can also find weakness of reliability in design and put forward the corresponding prevention and solution strategy. Finally, engine reliability can be improved and reliable operation of engine in the flight test can be insured. The paper mainly analyses many kinds of fault models happened possibly in the ground and flight test stages of some missile engine development, the causes of fault and the influence for the engine running and flight test, and lists the FMEA table of this type engine sample development stage.
(2)Using the FMEA method of missile engine, we can make FMEA analysis for the potential rupture fault mode of high pressure turbine blade. Based on the engine wreckage inspection after disassemble, the break analysis of high pressure turbine rotor blade, engine performance calculations, control system working analysis and engine life-span analysis etc., we have studied the causes and mechanism of fault, and get the conclusions and causes of some engine’s high pressure turbine blade break taking place in flight test.
(3)In the gas turbine fault diagnosis, we should make full use of much information, but not just pay attention to a sort of information. From the angle of diagnostics, any diagnosis information is ambiguous and inaccurate. And it is half-baked that any single information is to reflect the behavior of any kind of diagnostic object. We should get multidimensional information about the same object from many aspects, fuse and use them, so that we can carry out the more reliable and accurate monitoring and diagnosis for engine. The paper puts forward a kind of fusion diagnosis method based on integrated neural network, and realizes the engine wear fault fusion diagnosis aiming at the wear fault diagnosis problem of gas turbine engine.
(4)The expert system based on rules is the best successful field in artificial intelligence application. In this paper, we apply the diagnosis method based on knowledge rule into wear fault diagnosis of gas turbine engine, and build knowledge base, and design reversed reasoning machine, and study the method of knowledge acquisition automatically based on rough set theory. This method overcomes the problem that it is difficult to obtain knowledge in rule-based expert system effectively. At the same time, according to the change of fault symptom in practical case, we advance a dynamic flexible diagnosis method based on extensible knowledge base.
(5) Because case acquisition is easier than rule acquisition, so case-based reasoning (CBR) method is the most effective tool to build expert system. In this paper, we study a gas turbine engine troubleshooting expert system based on CBR. Firstly, we build case base; then combining character type fields matching technique with KNN method, advance case searching model aiming at engine troubleshooting cases; finally, we study the case modification method based on difference driving.
(6) Because the import and transfer characteristics of gas turbine engine complex system cannot be usually obtained, and it is difficult to forecast engine’s export in future. Then we can only use time series analysis method combining with system analysis, and use“system”to process dynamic data, and then obtain system mathematical model. And using this model to identify system and forecast the future development trend can come true. In this paper, using NN forecast method, we advance the trend forecast method based on structure-adaptive NN, and apply it into the trend forecasts of engine performance and wear.
引文
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