外文翻译--航空发动机状态监测系统设计研究
航空发动机诊断与健康管理系统设计
航空发动机诊断与健康管理系统设计简介：航空发动机作为飞机的“心脏”，其正常运行对于飞行安全至关重要。
然而，发动机在长期运行过程中可能会出现各种故障和异常情况，需要及时进行诊断和健康管理。
航空发动机诊断与健康管理系统的设计旨在利用先进的技术手段，实现对发动机状态的实时监测、故障诊断和健康管理，提高飞行安全性和飞机的可靠性。
一、系统概述航空发动机诊断与健康管理系统（Aircraft Engine Diagnosis and Health Management System，简称AE-D&HMS）是基于先进传感技术和数据分析算法的一个综合性系统。
它能够对发动机的状态进行实时监测，自动识别故障和异常情况，并提供相应的健康管理策略，以保证发动机的正常运行。
AE-D&HMS由四个主要模块组成，包括数据采集模块、数据处理与分析模块、故障诊断模块和健康管理模块。
二、数据采集模块数据采集模块是AE-D&HMS的基础，它通过各类传感器采集发动机运行时的数据，并实时传输到系统主机。
该模块包括传感器布置、数据采集和数据传输三个主要步骤。
1. 传感器布置：针对航空发动机的结构和工作原理，选择适合的传感器，并将其布置在发动机的关键部位。
传感器的种类包括温度传感器、压力传感器、振动传感器等。
2. 数据采集：传感器将采集到的数据转化为数字信号，并通过数据采集设备进行采集。
数据采集设备需要具备高精度、高采样率和抗干扰能力。
3. 数据传输：采集到的数据需要通过安全可靠的通信手段传输至系统主机。
常见的通信手段包括有线传输和无线传输。
有线传输稳定可靠，但需要布线，而无线传输灵活方便，但存在传输延迟等问题。
三、数据处理与分析模块数据处理与分析模块是AE-D&HMS的核心，它对传感器采集到的原始数据进行处理和分析，提取有价值的信息，并形成发动机状态的数字模型。
1. 数据预处理：对采集到的原始数据进行去噪、滤波、校正等处理，确保数据的准确性和可靠性。
航空发动机状态监视_故障诊断研究及验证
本文提出了发动机状态监视、 故障诊断的理论方 法， 重点介绍了含有健康参数的发动机建模； 在模型 的基础上设计了用于监视和故障诊断的卡尔曼滤波 器； 准确估计出反应发动机运行状态的不可测参数 ， 并用一组卡尔曼滤波器诊断了传感器故障 ； 最后介绍 了该部分机载系统的原理样机软硬件配置并进行了 进 仿真。仿真结果表明该硬件平台满足软件的需求 ， 行了理论验证。
Table 1
State variable Nl Nh
State variables，health parameters，actuators，
Health parameters Fan efficiency Hpc efficiency Lpt efficiency Hpt efficiency Unmeasured parameters FN SMC SMF
1
引
言
基于模型的状态监视、 故障诊断结构图如图 1 所 故障诊断是发动机健康管理项目的重 示。状态监视、 点， 其意义在于能够对推进系统性能 、 可操作性、 安全 性和可靠性起到重要作用。 NASA 有大量的研究机 构和公司对此进行研究， 并成功地运用在美国空军 C17 飞机上。目前中国也在此方面进行了大量的研 ， 航空发动机维修已转向“以可靠性为中心 ” 的 维修思想， 相应的维修方式也转向状态监视、 视情维 究
薛 薇，郭迎清，李 睿
（ 西北工业大学 动力与能源学院，陕西 西安 710072 ） 摘
*
要： 提出了发动机状态监视 、故障诊断的理论方法并搭建了该系统的软硬件平台，为建立机载发动机健
康管理系统奠定了坚实的基础 。首先，建立并验证了含有健康参数的发动机线性化模型，在模型的基础上设计了 用于故障诊断的卡尔曼滤波器； 其次，用设计好的滤波器可以准确估计出反应发动机运行状态的不可测参数； 随 后又用了一组卡尔曼滤波器诊断 、隔离了传感器故障； 最后，介绍了该部分机载系统原理样机的软硬件配置，并 进行了算法平台验证，从操作和实现方式上验证了软硬件平台 。该设计满足算法需求且界面人性化 、易于操作。 关键词： 航空发动机； 健康蜕化； 不可测参数； 状态监视； 故障诊断 中图分类号： V233. 7 文献标识码： A 4055 （ 2011 ） 02027105 文章编号： 1001-
航空发动机控制状态维持系统研究
航空发动机控制状态维持系统研究在现代航空的发展中，航空发动机控制技术变得越来越重要。
这种技术不仅可以使航空发动机性能提高，而且可以保证安全、可靠运行。
与此同时，由于航空发动机运转状态的影响，加上一些外部干扰因素的影响，调节航空发动机的状态的维持系统也变得至关重要。
这篇文章中，将探讨航空发动机控制状态维持系统的研究情况。
1. 概述航空发动机控制状态维持系统是由多种技术和知识构成的。
例如，航空发动机控制技术、数字信号处理技术、电路设计技术、数据库管理技术、机械制造技术、测试技术等。
在实际应用中，航空发动机控制状态维持系统的效果将与多种因素相关，如传感器的质量、控制算法的设计、控制电路的稳定性等。
2. 传感器设计在航空发动机控制状态维持系统中，传感器的设计起着至关重要的作用。
传感器必须满足航空发动机运作环境的严厉条件，避免受到外部干扰。
这需要传感器能够抵御多种不利的环境因素，如高温、高压、高速等。
此外，传感器还必须能够快速、准确地采集数据，这对传感器的灵敏度和精度提出了更高的要求。
3. 控制算法设计航空发动机控制状态维持系统的控制算法的设计要求具有高精度和高稳定性。
这种算法需要根据航空发动机运行的实际情况，构建合适的数学模型，并根据模型来选择合适的控制策略。
同时，控制算法应该能够应对不同的工况条件，如高海拔、高温等。
4. 控制电路设计在控制算法的基础上，需要对航空发动机控制状态维持系统的控制电路进行设计。
设计提出的要求与传感器类似，控制电路需要具有稳定性和可靠性。
控制电路的性能直接影响了控制算法的实际应用效果。
为了保证航空发动机控制状态维持系统的正常运行，控制电路需要具有超高的抗干扰性能，能够快速地收集数据，实时地控制航空发动机的状态。
5. 现有研究目前，已经有很多团队开始研究航空发动机控制状态维持系统。
其中，一些企业正在根据航空发动机的性能要求，开发出专业的航空发动机控制状态维持系统。
一方面，他们研发有效的传感器，能够满足航空发动机运作环境的要求，实时监控航空发动机状态；另一方面，这些企业研究并优化控制算法和控制电路，以确保系统具有较高的性能和可靠性。
航空发动机故障诊断系统的研究与设计
航空发动机故障诊断系统的研究与设计第一章绪论航空发动机故障诊断系统的研究与设计是目前航空工业中一个重要的课题。
航空发动机的重要性不言而喻，它是飞机运行的核心设备，如果出现故障，会严重影响飞行安全。
因此，构建航空发动机故障诊断系统，可以及时发现发动机的故障，保障飞机在空中的安全，对提升航空工业的技术水平和信誉度，具有非常重要的意义。
第二章航空发动机故障诊断系统的基础知识1.航空发动机工作原理航空发动机是指专门用于飞机、飞艇、导弹等航空器上的动力装置，它的主要功能是将燃油燃烧成高温高压的气体，通过流经涡轮叶片，带动涡轮转子、轴和风扇叶片等设备旋转，提供推力，驱动飞机前进。
2.航空发动机故障类型航空发动机故障类型包括机械故障、电气故障、燃油系统故障、润滑系统故障、冷却系统故障等。
3.航空发动机故障诊断方法航空发动机故障诊断方法包括传统的人工检测和电子辅助检测两种。
传统的人工检测主要依靠机务人员的经验来进行，效率低、误差大，难以满足航空工业日益提高的安全、可靠性和保障性的要求。
电子辅助检测主要依靠电子传感器和信号处理器，通过检测各种传感器方式获得的数据，实现故障预测、诊断和修复等功能。
第三章航空发动机故障诊断系统的设计1.系统设计思路航空发动机故障诊断系统采用电子辅助检测的方法，结合机械检测，通过传统的电子控制系统收集和分析发动机的工作状态，进行自诊断和故障判断，然后根据故障类型、程度和位置等信息，自动拟定维修计划。
2.系统设计流程航空发动机故障诊断系统设计流程，包括传感器数据采集、实时数据传输、数据上传和中央数据处理四个部分。
系统的设计主要包括以下几个环节：数据采集和分析、模型建立和故障检测、故障诊断和维修辅助等。
3.系统设计原则航空发动机故障诊断系统的设计原则包括高可靠性、高精度、高效率、适应性好、接口兼容性强等。
第四章航空发动机故障诊断系统实现1.硬件实现航空发动机故障诊断系统采用了单片机、传感器、数据采集卡、通讯接口等硬件设备，实现对发动机的实时监控、数据采集、数据处理和反馈。
飞机发动机性能监控系统设计与优化
飞机发动机性能监控系统设计与优化随着航空工业的不断发展，飞机依托发动机的性能和稳定性来保证航行安全。
发动机性能监控系统（Engine Performance Monitoring System, EPMS）是确保飞机发动机安全运转和保障飞机安全的关键技术之一。
在越来越注重机场和航空器安全的今天，设计一套高效优秀的EPMS, 对于提高机场安全技术水平，确保机场运营顺利实现持续发展具有重要的意义。
一、EPMS的基本概念发动机性能监控系统，简称EPMS，是指对发动机的工作参数和状态进行实时监控、分析和处理的系统，目的是实现对发动机的全面掌控和优化。
EPMS主要功能由四部分组成：数据采集、数据传输、数据处理和数据分析。
EPMS采集发动机的参数数据并实时传输数据到数据处理中心。
EPMS的数据处理中心通过监控和分析对发动机状态、工作性能和安全保障进行统一管理。
二、EPMS的设计原则在EPMS设计时，应该考虑设备的性能要求、数据接口规范、系统的稳定性、安全性和完整性等多方面问题。
应该遵循如下设计原则：（一）极简原则：EPMS系统应该尽可能减少对发动机的影响，同时降低系统的复杂度，提高系统可靠性。
（二）灵活性原则：EPMS系统应该支持多种特定的发动机型号及版本，同时避免对用户的操作和维护产生过大的影响。
（三）兼容性原则：EPMS系统应该与现有的机载设备和航空系统紧密配合，支持多种数据格式和数据传输方式。
（四）标准化原则：EPMS系统应该遵循相应的技术标准，确保系统的互通性、可扩展性和可维护性。
（五）稳定性原则：EPMS系统应该具备良好的稳定性、可靠性和容错性，能够对系统故障和数据错误完成自动屏蔽和修正。
三、EPMS的优化方法在EPMS设计过程中，应该不断优化EPMS各个部分的性能、功能和安全，以确保EPMS系统一直处于良好的状态。
在此，我们可以采用如下几种优化方法：（一）数据加密和传输安全：在EPMS的数据传输阶段，应该采用有效的加密技术，加强数据传输的安全性，避免数据泄露和篡改。
(论文)民航发动机控制系统故障在线监测方法研究
毕业设计题目民航发动机控制系统故障在线监测方法研究学生姓名学号学院专业班级指导教师民航发动机控制系统故障在线监测方法研究摘要航空发动机控制系统是航空发动机的安全关键系统，保证了航空发动机在各种可能的条件下安全可靠地工作，为了保持可靠性，需要对其进行在线监测并且隔离出故障。
异常监测算法的研究能为维修人员提供直接有效的信息，能有效保障安全和降低维修成本。
本文以CFM56-7B控制系统为研究对象，针对最可能发生故障的传感器部分开展了方法研究，提出了基于多元状态估计和极限学习机的传感器信号在线监测方法，利用译码得到的QAR数据进行了验证。
通过对正常航班的训练得到模型，然后对正常测试数据进行了故障模拟，并对残差进行了序贯概率比检验，最后开发了MATLAB GUI交互界面，该图形界面整合了数据的训练和测试、故障模拟及残差检验。
关键词：CFM56-7B，QAR数据，传感器，多元状态估计，极限学习机目录摘要 (ⅰ)Abstract (ⅱ)第一章绪论 (1)1.1研究背景及意义 (1)1.2国内外研究现状 (2)1.3论文主要内容 (3)第二章CFM56-7B航空发动机控制系统 (5)2.1CFM56-7B航空发动机控制原理 (5)2.2CFM56-7B航空发动机控制系统组成 (6)2.2.1 电子控制器EEC (7)2.2.2敏感元件传感器 (7)2.2.3放大元件和执行机构 (14)2.3CFM56-7B航空发动机控制系统主要故障 (15)第三章航空发动机控制系统传感器故障检测算法研究 (16)3.1多元状态估计（MSET） (16)3.1.1MSET基本原理 (16)3.1.2在线异常检测步骤 (17)3.2极限学习机（ELM） (18)3.3基于序贯概率比（SPRT）的异常检测 (20)3.3.1序贯概率比 (20)3.3.2残差检验的一般步骤 (21)第四章MSET和ELM在CFM56-7B控制系统传感器上的故障监测 (23)4.1QAR数据 (23)4.2基于MSET的全航班多飞行阶段的传感器故障监测 (23)4.2.1数据的预处理 (23)4.2.1实例检测 (25)4.2.3CFM56-7B传感器故障模拟及在线监测 (32)4.3基于ELM的传感器故障监测 (36)4.3.1数据的训练及人工神经网络的建立 (36)4.3.2CFM56-7B传感器故障模拟及监测 (38)4.4两种监测方法的比较 (40)4.5CFM56-7B传感器在线监测人机交互GUI界面的设计 (42)第五章总结与展望 (45)5.1 总结 (45)5.1 展望 (45)参考文献 (46)致谢 (48)第一章绪论1.1 研究背景及意义我国民航业正进入高速发展的新时期，中国作为一个航空大国，其航空安全关系到我国的整个航空工业体系和经济的发展。
航空发动机状态监控和预测性维护应用研究
收稿日期：２０２２－０６－０６引用格式：廖鹏程，李昂，王骁，等．航空发动机状态监控和预测性维护应用研究［Ｊ］．测控技术，２０２３，４２（５）：８５－９０．ＬＩＡＯＰＣ，ＬＩＡ，ＷＡＮＧＸ．ＡｐｐｌｉｅｄＲｅｓｅａｒｃｈｏｆＳｔａｔｕｓＭｏｎｉｔｏｒｉｎｇａｎｄＰｒｅｄｉｃｔｉｖｅＭａｉｎｔｅｎａｎｃｅｆｏｒＡｅｒｏｅｎｇｉｎｅ［Ｊ］．Ｍｅａｓｕｒｅｍｅｎｔ＆ＣｏｎｔｒｏｌＴｅｃｈｎｏｌｏｇｙ，２０２３，４２（５）：８５－９０．航空发动机状态监控和预测性维护应用研究廖鹏程，李 昂，王 骁（航空工业陕西千山航空电子有限责任公司，陕西西安 ７１００６５）摘要：为了深化飞参数据的应用价值，通过研究发动机转动件故障预测、剩余寿命预测以及气路健康等，为发动机保障决策和预测性维护提供参考。
采用经验模态分解（ＥＭＤ）结合相对向量机（ＲＶＭ）、灰度模型（ＧＭ）用于发动机转动件、气路监测的状态监控和故障预测，选取波音某型飞机故障数据验证了模型的准确性，平均绝对百分比误差（ＭＡＰＥ）能达到８．４６％；采用卡尔曼滤波（ＫＦ）结合梯度提升决策树（ＧＢＤＴ）的方法对数据进行降噪并预测剩余寿命，通过美国国家航空航天局（ＮＡＳＡ）的航空发动机仿真数据集验证了模型能达到９１ ３％的准确率；采用核主成分分析（ＫＰＣＡ）结合深度置信网络（ＤＢＮ）的方法建立发动机气路健康监控模型，经过大量ＱＡＲ数据验证和测试，预测相对误差为０ ４３％。
针对基于数据挖掘的航空发动机故障诊断算法开展研究，设计了相应的算法，开展了实验验证，通过有效的数据预处理和模型参数调节，使得故障诊断性能达到较高水准，为航空发动机的预测性维护提供了重要参考。
关键词：特征提取；深度学习；故障预测；健康管理；剩余寿命预测中图分类号：ＴＰ３９１；Ｖ２４１ 文献标志码：Ａ 文章编号：１０００－８８２９（２０２３）０５－００８５－０６ｄｏｉ：１０．１９７０８／ｊ．ｃｋｊｓ．２０２３．０５．０１２ＡｐｐｌｉｅｄＲｅｓｅａｒｃｈｏｆＳｔａｔｕｓＭｏｎｉｔｏｒｉｎｇａｎｄＰｒｅｄｉｃｔｉｖｅＭａｉｎｔｅｎａｎｃｅｆｏｒＡｅｒｏｅｎｇｉｎｅＬＩＡＯＰｅｎｇｃｈｅｎｇ牞ＬＩＡｎｇ牞ＷＡＮＧＸｉａｏ牗ＡＶＩＣＳｈａａｎｘｉＱｉａｎｓｈａｎＡｖｉｏｎｉｃｓＣｏ．牞Ｌｔｄ．牞Ｘｉ ａｎ７１００６５牞ｃｈｉｎａ牘Ａｂｓｔｒａｃｔ牶Ｉｎｏｒｄｅｒｔｏｄｅｅｐｅｎｔｈｅａｐｐｌｉｃａｔｉｏｎｖａｌｕｅｏｆｆｌｉｇｈｔｐａｒａｍｅｔｅｒｄａｔａ牞ｒｅｆｅｒｅｎｃｅｉｓｐｒｏｖｉｄｅｄｆｏｒｅｎｇｉｎｅｓｕｐｐｏｒｔｄｅｃｉｓｉｏｎ ｍａｋｉｎｇａｎｄｐｒｅｄｉｃｔｉｖｅｍａｉｎｔｅｎａｎｃｅｔｈｒｏｕｇｈｒｅｓｅａｒｃｈｏｎｆａｕｌｔｐｒｅｄｉｃｔｉｏｎｏｆｅｎｇｉｎｅｒｏｔａｔｉｎｇｐａｒｔｓ牞ｒｅｓｉｄｕａｌｌｉｆｅｐｒｅｄｉｃｔｉｏｎ牞ａｎｄｇａｓｐａｔｈｈｅａｌｔｈ．Ｅｍｐｉｒｉｃａｌｍｏｄｅｄｅｃｏｍｐｏｓｉｔｉｏｎ牗ＥＭＤ牘ｃｏｍｂｉｎｅｄｗｉｔｈｒｅｌａ ｔｉｖｅｖｅｃｔｏｒｍａｃｈｉｎｅ牗ＲＶＭ牘ａｎｄｇｒａｙｍｏｄｅｌ牗ＧＭ牘ａｒｅｕｓｅｄｆｏｒｃｏｎｄｉｔｉｏｎｍｏｎｉｔｏｒｉｎｇａｎｄｆａｕｌｔｐｒｅｄｉｃｔｉｏｎｏｆｅｎ ｇｉｎｅｒｏｔａｔｉｎｇｐａｒｔｓａｎｄｇａｓｐａｔｈｍｏｎｉｔｏｒｉｎｇ．ＴｈｅｆａｕｌｔｄａｔａｏｆａＢｏｅｉｎｇａｉｒｃｒａｆｔａｒｅｓｅｌｅｃｔｅｄｔｏｖｅｒｉｆｙｔｈｅａｃｃｕｒａ ｃｙｏｆｔｈｅｍｏｄｅｌ牞ａｎｄｔｈｅａｖｅｒａｇｅｍｅａｎａｂｓｏｌｕｔｅｐｅｒｃｅｎｔａｇｅｅｒｒｏｒ牗ＭＡＰＥ牘ｒｅａｃｈｅｓ８．４６％．Ｋａｌｍａｎｆｉｌｔｅｒ牗ＫＦ牘ｃｏｍｂｉｎｅｄｗｉｔｈｇｒａｄｉｅｎｔｂｏｏｓｔｉｎｇｄｅｃｉｓｉｏｎｔｒｅｅ牗ＧＢＤＴ牘ｉｓｕｓｅｄｔｏｄｅｎｏｉｓｅｔｈｅｄａｔａａｎｄｐｒｅｄｉｃｔｔｈｅｒｅｍａｉｎｉｎｇｌｉｆｅ．Ｔｈｅａｃｃｕｒａｃｙｏｆｔｈｅｍｏｄｅｌｉｓｖｅｒｉｆｉｅｄｔｏｂｅ９１．３％ｂｙＮＡＳＡ ｓａｅｒｏｅｎｇｉｎｅｓｉｍｕｌａｔｉｏｎｄａｔａｓｅｔ．Ｔｈｅｍｅｔｈｏｄｏｆｋｅｒｎｅｌｐｒｉｎｃｉｐａｌｃｏｍｐｏｎｅｎｔａｎａｌｙｓｉｓ牗ＫＰＣＡ牘ｃｏｍｂｉｎｅｄｗｉｔｈｄｅｅｐｃｏｎｆｉｄｅｎｃｅｎｅｔｗｏｒｋ牗ＤＢＮ牘ｉｓｕｓｅｄｔｏｅｓ ｔａｂｌｉｓｈｔｈｅｅｎｇｉｎｅｇａｓｐａｔｈｈｅａｌｔｈｍｏｎｉｔｏｒｉｎｇｍｏｄｅｌ．ＡｆｔｅｒａｌａｒｇｅｎｕｍｂｅｒｏｆＱＡＲｄａｔａｖａｌｉｄａｔｉｏｎａｎｄｔｅｓｔｉｎｇ牞ｔｈｅｐｒｅｄｉｃｔｉｏｎｒｅｌａｔｉｖｅｅｒｒｏｒｉｓ０．４３％．Ｒｅｓｅａｒｃｈｉｓｃｏｎｄｕｃｔｅｄｏｎａｅｒｏｅｎｇｉｎｅｆａｕｌｔｄｉａｇｎｏｓｉｓａｌｇｏｒｉｔｈｍｓｂａｓｅｄｏｎｄａｔａｍｉｎｉｎｇ牞ｃｏｒｒｅｓｐｏｎｄｉｎｇａｌｇｏｒｉｔｈｍｓａｒｅｄｅｓｉｇｎｅｄ牞ａｎｄｅｘｐｅｒｉｍｅｎｔａｌｖｅｒｉｆｉｃａｔｉｏｎｉｓｃｏｎｄｕｃｔｅｄ．Ｔｈｒｏｕｇｈｅｆｆｅｃ ｔｉｖｅｄａｔａｐｒｅｐｒｏｃｅｓｓｉｎｇａｎｄｍｏｄｅｌｐａｒａｍｅｔｅｒａｄｊｕｓｔｍｅｎｔ牞ｆａｕｌｔｄｉａｇｎｏｓｉｓｐｅｒｆｏｒｍａｎｃｅｒｅａｃｈｅｓａｈｉｇｈｌｅｖｅｌ牞ｐｒｏｖｉｄｉｎｇｉｍｐｏｒｔａｎｔｒｅｆｅｒｅｎｃｅｖａｌｕｅｆｏｒｐｒｅｄｉｃｔｉｖｅｍａｉｎｔｅｎａｎｃｅｏｆａｅｒｏｅｎｇｉｎｅｓ．Ｋｅｙｗｏｒｄｓ牶ｆｅａｔｕｒｅｅｘｔｒａｃｔｉｏｎ牷ｄｅｅｐｌｅａｒｎｉｎｇ牷ｆａｕｌｔｐｒｅｄｉｃｔｉｏｎ牷ｈｅａｌｔｈｍａｎａｇｅｍｅｎｔ牷ｒｅｍａｉｎｉｎｇｌｉｆｅｐｒｅｄｉｃｔｉｏｎ 在参考国内外研究现状的基础上，分别对航空发动机转动件故障预测、剩余寿命预测和气路健康状态监控等进行了探索研究。
航空发动机健康监测与维修技术研究
航空发动机健康监测与维修技术研究航空发动机是飞行器的心脏，它的性能和可靠性对于飞行器的安全、经济和环保等方面具有至关重要的作用。
随着航空业的发展，航空发动机的性能、功率和可靠性要求也越来越高。
为了保证航班的安全和航空公司的经济效益，对航空发动机健康监测与维修技术的研究成为了重要的任务。
一、航空发动机健康监测技术航空发动机健康监测技术（Engine Health Monitoring，EHM）是指通过各种传感器采集航空发动机各项性能参数（如转速、温度、压力等），对发动机进行参数分析、故障诊断、寿命估计和预警通知等。
航空发动机健康监测技术的主要功能包括：1. 提高航空发动机的可靠性和安全性。
航空发动机健康监测技术可以实时监测航空发动机的状态，发现和诊断故障，及时采取维修措施，提高航空发动机的可靠性和安全性。
2. 降低维修成本和维修时间。
航空发动机健康监测技术可以提供发动机状态的详细信息，有助于维修人员进行准确的故障分析和维修措施，从而降低维修成本和维修时间。
3. 延长航空发动机的使用寿命。
通过航空发动机健康监测技术可以实时监测航空发动机的疲劳状况和寿命剩余，有助于延长航空发动机的使用寿命。
二、航空发动机维修技术航空发动机维修技术是指对航空发动机进行维修保养、检修和改装等工作的技术。
航空发动机维修技术的主要任务包括：确定故障原因、采取适当维修措施、检查维修效果和评估维修结果。
航空发动机维修技术的发展趋势主要体现在以下几个方面：1. 维修技术逐步向数字化、智能化方向发展。
现代航空发动机一般都配备了各种传感器和监测设备，可以实现数字化传输和数据处理，未来的维修技术会更加智能化。
2. 维修技术将更加关注环保问题。
随着环保意识的不断增强，航空发动机维修技术将更加注重环保问题，采用更加环保的材料和工艺，减少对环境的污染。
3. 不断提高维修效率和质量。
随着航空业的发展，航线和飞机数量也在不断增加，维修效率和质量将成为维修技术发展的重要目标。
机械状态监测和故障诊断的最新进展 毕业论文_英文论文及翻译
Recent Progress on Mechanical Condition Monitoring and Fault diagnosisChenxing Sheng, Zhixiong Li, Li Qin, Zhiwei Guo, Y uelei Zhang Reliability Engineering Institute, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, P. R. China Huangpi Campus, Air Force Radar Academy, Wuhan 430019, P. R. ChinaAbstractMechanical equipments are widely used in various industrial applications. Generally working in severe conditions, mechanical equipments are subjected to progressive deterioration of their state. The mechanical failures account for more than 60% of breakdowns of the system. Therefore, the identification of impending mechanical fault is crucial to prevent the system from malfunction. This paper discusses the most recent progress in the mechanical condition monitoring and fault diagnosis. Excellent work is introduced from the aspects of the fault mechanism research, signal processing and feature extraction, fault reasoning research and equipment development. An overview of some of the existing methods for signal processing and feature extraction is presented. The advantages and disadvantages of these techniques are discussed. The review result suggests that the intelligent information fusion based mechanical fault diagnosis expert system with self-learning and self-updating abilities is the future research trend for the condition monitoring fault diagnosis of mechanical equipments.© 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011]Keywords: Condition monitoring; Fault diagnosis; Vibration; Signal processing1. IntroductionWith the development of modern science and technology, machinery and equipment functions are becoming more and more perfect, and the machinery structure becomes more large-scale, integrated, intelligent and complicated. As a result, the component number increases significantly and the precision requirement for the part mating is stricter. The possibility and category of the related component failures therefore increase greatly. Malignant accidents caused by component faults occur frequently all over the world, and even a small mechanical fault may lead to serious consequences. Hence, efficient incipient fault detection and diagnosis are critical to machinery normal running. Although optimization techniques have been carried out in the machine design procedure and the manufacturing procedure to improve the quality of mechanical products, mechanical failures are still difficult to avoid due to the complexity of modern equipments. The condition monitoring and fault diagnosis based on advanced science and technology acts as an efficient mean to forecast potential faults and reduce the cost of machine malfunctions. This is the so-called mechanical equipment fault diagnosis technology emerged in the nearly three decades [1, 2].Mechanical equipment fault diagnosis technology uses the measurements of the monitored machinery in operation and stationary to analyze and extract important characteristics to calibrate the states of the key components. By combining the history data, it can recognize the current conditions of the key components quantitatively, predicts the impending abnormalities and faults, and prognoses their future condition trends. By doing so, the optimized maintenance strategies can be settled, and thus the industrials can benefit from the condition maintenance significantly [3, 4].The contents of mechanical fault diagnosis contain four aspects, including fault mechanism research, signal processing and feature extraction, fault reasoning researchand equipment development for condition monitoring and fault diagnosis. In the past decades, there has been considerable work done in this general area by many researchers. A concise review of the research in this area has been presented by [5, 6]. Some landmarks are discussed in this paper. The novel signal processing techniques are presented. The advantages and disadvantages of these new signal processing and feature extraction methods are discussed in this work. Then the fault reasoning research and the diagnostic equipments are briefly reviewed. Finally, the future research topics are described in the point of future generation intelligent fault diagnosis and prognosis system.2. Fault Mechanism ResearchFault Mechanism research is a very difficult and important basic project of fault diagnosis, same as the pathology research of medical. American scholar John Sohre, published a paper on "Causes and treatment of high-speed turbo machinery operating problems (failure)", in the United States Institute of Mechanical Engineering at the Petroleum Mechanical Engineering in 1968, and gave a clear and concise description of the typical symptoms and possible causes of mechanical failure. He suggested that typical failures could be classified into 9 types and 37 kinds [7]. Following, Shiraki [8] conduced considerable work on the fault mechanism research in Japan during 60s-70s last century, and concluded abundant on-site troubleshooting experience to support the fault mechanism theory. BENTLY NEV ADA Corporation has also carried out a series experiments to study the fault mechanism of the rotor-bearing system [9]. A large amount of related work has been done in China as well. Gao et al. [10] researched the vibration fault mechanism of the high-speed turbo machinery, investigated the relationship between the vibration frequency and vibration generation, and drew up the table of the vibration fault reasons, mechanism and recognition features for subsynchronous, synchronous and super-synchronous vibrations. Based on the table they proposed, they have classified the typical failures into 10 types and 58 kinds, and provided preventive treatments during the machine design and manufacture, Installation and maintenance, operation, and machine degradation. Xu et al. [11] concluded the common faults of the rotational machines. Chen et al. [12] used the nonlinear dynamics theory to analyze the key vibration problems of the generator shaft. They established a rotor nonlinear dynamic model for the generator to comprehensively investigate the rotor dynamic behavior under various influences, and proposed an effective solution to prevent rotor failures. Yang et al. [13] adopted vibration analysis to study the fault mechanism of a series of diesel engines. Otherresearchers have done a lot in the fault mechanism of mechanics since 1980s, and have published many valuable papers to provide theory and technology supports in the application of fault diagnosis systems [14-18]. However, most of the fault mechanism research is on the qualitative and numerical simulation stage, the engineering practice is difficult to implement. In addition, the fault information often presents strong nonlinear, non stationary and non Gaussian characteristics, the simulation tests can not reflect these characteristics very accurately. The fault diagnosis results and the application possibility may be influenced significantly. As a result, the development of the fault diagnosis technique still faces great difficulties.3. Advanced Signal Processing and Feature Extraction MethodsAdvanced signal processing technology is used to extract the features which are sensitive to specific fault by using various signal analysis techniques to process the measured signals. Condition information of the plants is contained in a wide range of signals, such as vibration, noise, temperature, pressure, strain, current, voltage, etc. The feature information of a certain fault can be acquired through signal analysis method, and then fault diagnosis can be done correspondingly. To meet the specific needs of fault diagnosis, fault feature extraction and analysis technology is undergoing the process from time domain analysis to Fourier analysis-based frequency-domain analysis, from linear stationary signal analysis to nonlinear and nonstationary analysis, from frequency-domain analysis to time-frequency analysis.Early research on vibration signal analysis is mainly focused on classical signal analysis which made a lot of research and application progress. Rotating mechanical vibration is usually of strong harmonic, its fault is also usually registered as changes in some harmonic components. Classical spectrum analysis based on Fourier transform (such as average time-domain techniques, spectrum analysis, cepstrum analysis and demodulation techniques) can extract the fault characteristic information effectively, thus it is widely used in motive power machine, especially in rotating machinery vibration monitoring and fault diagnosis. In a manner of speaking, classical signal analysis is still the main method for mechanical vibration signal analysis and fault feature extraction. However, classical spectrum analysis also has obvious disadvantages. Fourier transform reflects the overall statistical properties of a signal, and is suitable for stationary signal analysis. In reality, the signals measured from mechanical equipment are ever-changing, non-stationary, non-Gaussian distribution and nonlinear random. Especially when the equipment breaks down, this situation appears to be more prominent. For non-stationary signal, some time-frequency detailscan not be reflected in the spectrum and its frequency resolution is limited using Fourier transform. New methods need to be proposed for those nonlinearity and non-stationary signals. The strong demand from the engineering practice also contributes to the rapid development of signal analysis. New analytical methods for non-stationary signal and nonlinear signal are emerging constantly, which are soon applied in the field of machinery fault diagnosis. New methods of signal analysis are main including time-frequency analysis, wavelet analysis, Hilbert-Huang transform, independent component analysis, advanced statistical analysis, nonlinear signal analysis and so on. The advantages and disadvantages of these approaches are discussed below.4. Research on Fault ReasoningAt present, many methods are adopted in the process of diagnostic reasoning. According to the subject systems which they belong to, the fault diagnosis can be divided into three categories: (1) the fault diagnosis based on control model; (2) the fault diagnosis based on pattern recognition; (3) the fault diagnosis based on artificial intelligence. Among them, the fault diagnosis based on control model needs to establish model through theoretic or experimental methods. The changes of system parameters or system status could directly reflect the changes of equipments physical process, and hence it is able to provide basis for fault diagnosis. This technology refers to model establishment, parameters estimation, status estimation, application of observers, etc. Since it requires accurately system model, this method is not economically feasible for the complicated devices in the practice.Pattern recognition conducts cluster description for a series of process or events. It is mainly divided into statistical method and language structure method. The fault diagnosis of equipments could be recognized as the pattern recognition process, that is to say, it recognizes the fault based on the extraction of fault characteristics. There are many common recognition methods, including bayes category, distance function category, fuzzy diagnosis, fault tree analysis, grey theory diagnosis and so on. Recent years, some new technologies have been also applied in the field of the fault diagnosis of rotary machines, such as the combination of fuzzy set and neural network, the dynamic pattern recognition based on hidden markov model, etc.5. Research and Development of Fault Diagnosis DevicesFault diagnosis technology ultimately comes down to the actual devices, and at present research and development of fault diagnosis devices is in the following two directions: (1) Portable vibration monitoring and diagnosis (including data collector system), and (2) On-line condition monitoring and fault diagnosis system. Portable instrument is mainly adopted single-chip microcomputers to complete data acquisition, which has certain ability for signal analysis and fault diagnosis. On-line monitoring and diagnosis system is usually equipped with sensors, data acquisition, alarm and interlock protection, condition monitoring subsystem, etc. And it is also fitted with rich signal analysis and diagnosis software. These software include America BENTLY Corporation 3300, 3500 and DM2000 systems, America Westinghouse Company PDS system, the 5911 system developed by ENTECK and IRD Company, Japan Mitsubishi MHM system, the Danish B&K Company B&K 3450 COMPASS system, etc. China has also successively developed large on-line monitoring and fault diagnosis system, which has been put into use on steam turbine and other important equipments.Based on the realization of condition monitoring of equipments, network diagnostics center can monitor and diagnose the operation of equipments at any time through the network to achieve the long distance information transmission. The remote monitoring system can also achieve the collaborative diagnosis of production equipments, multiple diagnostic systems serve the same piece of equipment, and multiple devices share the same diagnostic system.6. ConclusionsTo achieve a dynamic system condition monitoring and fault diagnosis, primary task is the need to get enough reliable characteristic information from the system. Due to the fluctuation of the system itself and the environment disturbance, reliable signal collection is seriously affected. It is therefore very urgent for advanced signal processing technology to eliminate noise to get true signal. No matter classical or advance fault diagnosis techniques, they have achieved great progress in various applications. In the point of systematic view, every technology is a part of the whole diagnostic system, and the efficient fusion of these parts will provide best performance for the condition monitoring and fault diagnosis. Thus, the fault mechanism research, signal processing and feature extraction, fault reasoning research and equipment development will connect even tighter to form an effective fault diagnostic expert system in the future. To realize the expert system, the core issue is to break through the bottleneck of knowledge acquisition, update the data model in a reliable manner and provide good generalization ability of the expert system. By doing so, the fault diagnostic expert system can offer accurate estimation of the potential abnormalities, and prevent them before breaking out to ensure the normal operation of the machines. Hence, the loss caused by the machine breakdowns can be minimized significantly.AcknowledgementsThis project is sponsored by the grants from the National Natural Sciences Foundation of China(NSFC) (No. 50975213).References[1] Wu XK. The fault diagnosis based on information fusion theory and its application in internal combustion engine. Ph.D. thesis, Wuhan University of Technology, 1998.[2] Chen YR. Modern signal processing technology in the application of vibration diagnosis of internal combustion engine.Ph.D. thesis, Wuhan University of Technology, 1998.[3] Qu LS, He ZJ. Mechanical fault diagnostics. Shanghai: Shanghai Science and Technology Press, 1986.[4] Huang WH, Xia SB, Liu RY. Equipment fault diagnosis principle, technology and application. Beijing: Science Press, 1996.[5] Jayaswalt P, Wadhwani AK. Application of artificial neural networks, fuzzy logic and wavelet transform in fault diagnosis via vibration signal analysis: A review. Australian Journal of Mechanical Engineering 2009; 7: 157-172.[6] Daneshi-Far Z, Capolino GA, Henao H. Review of failures and condition monitoring in wind turbine generators. 19th International Conference on Electrical Machines. Rome, Italy; 2010. [7] Sohre JS. Trouble-shooting to stop vibration of centrifugal. Petrop Chem. Engineer 1968; 11: 22-23.[8] Shiraki T. Mechanical vibration lectures. Zhengzhou: Zhengzhou Mechanical Institute; 1984.[9] Bently DW. Forced subrotative speed dynamic action of rotating machinery. USA: ASME Publication, 74-pet-16.[10] Gao JJ. Research on high speed turbine machinery vibration fault mechanism and diagnostic method. Ph.D. thesis, Xi'an Jiaotong University, 1993.[11] Xu M, Zhang RL. Equipment fault diagnosis manual. Xi’an: Xi'an Jiaotong University Press, 1998.[12] Chen YS, Tian JY, Jin ZW, Ding Q. Theory of nonlinear dynamics and applied techniques of solving irregular operation of a large scale gas turbine in a comprehensive way. China Mechanical Engineering 1999; 10: 1063-68.[13] Yang JG, Zhou YC. Internal combustion engine vibration monitoring and fault diagnosis. Dalian: Dalian Maritime University Press, 1994.[14] Wang Y, Gao JJ, Xia SB. The study of causes and features of faults in supporting system for rotary machinery. Journal of Harbin Institute of Technology 1999; 31:104-6.[15] Liu SY, Song XP, Wen BC. Catastrophe in fault developing process of rotor system. Journal of Northeastern University (Natural Science) 2004; 17:159-162.[16] Han J, Zhang RL. Rotating machinery fault mechanism and diagnostic technique. Beijing: China Machine Press, 1997.[17] Chen AH. Research on some nonlinear fault phenomenon of rotating machinery. Ph.D. thesis, Central South University of Technology, 1997.[18]Zhang W, Zhang YX. Missile power system fault mechanism analysis and diagnosis technology. Xi’an: Northwest Indus trial University press, 2006.机械状态监测和故障诊断的最新进展Chenxing Sheng, Zhixiong Li, Li Qin, Zhiwei Guo, Y uelei Zhang武汉理工大学，能源与动力工程学院，可靠性工程研究所，中华人民共和国，武汉，430063空军雷达学院，黄陂校区，中华人民共和国，武汉，430019摘要机械设备被广泛应用于各种工业应用。
航空发动机状态监控系统研究
航空发动机状态监控系统研究郭迎清;李睿;薛薇【摘要】航空发动机的维修方式正在由定期维修向视情维修转变,可以在保证发动机可靠性的前提下,降低发动机的维护费用.状态监控系统的研究是开展航空发动机视情维修的关键步骤.借鉴国外典型的发动机状态监控系统,提出了发动机状态监控系统的结构、功能需求、监控事件的选择,实现了机载状态监控系统的原理样机及其仿真平台,对状态监控系统的研究有一定意义.【期刊名称】《航空发动机》【年(卷),期】2010(036)005【总页数】5页(P39-43)【关键词】航空发动机;视情维修;状态监控系统;原理样机;仿真平台【作者】郭迎清;李睿;薛薇【作者单位】西北工业大学,动力与能源学院,西安,710072;西北工业大学,动力与能源学院,西安,710072;西北工业大学,动力与能源学院,西安,710072【正文语种】中文1 引言航空发动机是飞机的重要部件，一旦发生事故，将会造成巨大损失。
因此，提高航空发动机的可靠性有着重要意义。
随着航空发动机结构的复杂化，其维护和保障费用日益增多，经济可承受性成为不可回避的问题[1]。
在提高发动机可靠性的前提下，减少发动机的维护和保障费用成为重要研究方向[2]。
传统的维修方式以定期维护为主，但这种方式没有考虑到发动机的个体健康状况不同。
部分发动机在较好的健康状况下就被维修，而部分发动机因为在维修前出现故障但未得到及时处理，使其可靠性不能得到较好保障，同时维护费用无法有效降低[3]。
解决这一问题的1种有效办法就是根据发动机的健康状况信息进行维护和故障的预测，以实现由定期维修向视情维修的转变[4]。
视情维修要求推进系统具有对故障进行预测并对自身的健康状态进行管理的能力。
实现发动机健康状况自动监控、故障预测诊断、使用寿命延长、安全工作保证和维护成本减少是研制新一代航空发动机的重要内容[5]。
发动机状态监控系统是开展视情维修的关键，也是发动机健康管理系统的重要组成部分。
航空发动机实时状态监测与预测方法研究
航空发动机实时状态监测与预测方法研究航空发动机在飞行中扮演着至关重要的角色，它们是飞机起飞、飞行和降落的关键部件。
但是，由于飞行环境的复杂性和高度不可预测性，航空发动机的状态监测和预测成为一项非常具有挑战性的任务。
然而，随着技术的不断进步和算法的不断完善，发动机实时状态监测与预测方法也在不断发展。
一、航空发动机实时状态监测技术航空发动机实时状态监测技术是一种通过传感器和数据采集系统实时监测发动机的工作状态并及时报警的技术。
该技术可以对发动机的振动、温度、压力、燃烧和电气参数等进行实时监测，同时还可以对发动机的故障进行检测和诊断。
通过实时监测，可以大大提高航班安全性和发动机的可靠性，降低事故风险。
此外，实时监测技术还可以帮助航空公司进行更好的维护安排和维修预测。
目前，市场上存在着多种航空发动机实时状态监测技术，包括基于物理模型、统计模型、神经网络和深度学习模型的监测技术。
其中，基于神经网络和深度学习模型的监测技术最为先进和有效。
这些技术可以通过数据挖掘、机器学习和模式识别等算法，综合分析多种发动机参数，提高监测准确性和预警能力。
此外，这些技术还可以根据发动机的状态变化，自动调整模型参数，实现自适应监测和预测。
二、航空发动机预测技术航空发动机预测技术是一种可以对发动机未来性能和故障进行预测的技术。
该技术可以利用历史数据、实时监测数据和模型预测算法，分析发动机在不同环境下的运行特征和健康状态，并预测未来的性能和故障。
通过预测技术，可以避免故障和不可预见的风险，降低航空公司的维修成本和停机时间，提高发动机的可靠性和寿命。
目前，航空发动机预测技术主要分为基于物理模型、统计模型、神经网络和深度学习模型的预测技术。
其中，基于神经网络和深度学习模型的预测技术最为先进和有效。
这些技术可以通过数据挖掘、机器学习和模式识别等算法，利用海量数据训练预测模型，并预测未来发动机的健康状况、寿命和性能变化。
此外，这些技术还可以通过模型自适应调整和可解释性分析，提高预测准确性和可靠性。
航空发动机故障监测诊断系统设计
系统工作原理
数据采集
系统通过传感器采集发动机的各项参数，如温度、 压力、转速等。
故障诊断
一旦发现异常情况，系统会进行故障诊断，确定 故障类型和位置，并发出报警信号。
ABCD
数据分析
采集的数据经过处理和分析，与正常值进行比较， 判断发动机的运行状态是否正常。
信息输出
系统将监测和诊断结果通过显示界面或数据接口 输出，供维护人员参考和使用。
用户界面设计
设计友好、直观的用户界面，便于用户进行 操作和监控。
05
系统实现与测试
系统集成与测试
硬件设备集成
01
将各种传感器、采集器、处理器等硬件设备按照系统设计要求
进行集成。
软件模块整合
02
将各个功能模块的软件进行整合，确保模块之间的数据传输和
功能协调。
系统测试环境搭建
03
搭建符合实际运行环境的测试平台，模拟发动机运行状态进行
专家经验
利用专家对发动机的知识和经验，建立故障诊 断知识库。
案例推理
通过比对历史故障案例，快速定位和诊断当前 故障。
规则推理
根据故障征兆和关联规则，进行故障推理和诊断。
基于人工智能的诊断
数据驱动
利用大量的发动机运行数据，通过机器学习和深度学 习算法，进行故障模式识别和分类。
自主学习
通过持续学习新的故障案例，不断优化诊断算法，提 高诊断准确性。
航空发动机故障监测诊断系 统设计
目录
• 系统概述 • 故障监测技术 • 诊断技术 • 系统设计 • 系统实现与测试
01
系统概述
系统定义与目标
定义
航空发动机故障监测诊断系统是一种用于监测和诊断航空发动机运行状态的电 子系统。
航空发动机监测技术研究
航空发动机监测技术研究在现代社会中，航空运输已经成为了人们生活和经济的重要组成部分。
而在每一次航班中，保障航班的安全是最为重要的。
其中又以航空发动机的安全和运转为关键点。
发动机运转状况的不稳定和出现故障将直接危及航班的安全。
因此，航空发动机监测技术的研究和应用对于现代化航空产业的发展和安全至关重要。
一、航空发动机的重要性航空发动机是飞机最为核心的部分，它的运转情况直接关系到飞机的起飞、飞行和降落。
而现代化的大型客机，其航空发动机数量达到了四个，其重要性不言而喻。
与此同时，航空发动机的复杂性和工作环境的极端性给发动机的性能监测和维护工作带来了很大的挑战。
二、航空发动机监测技术的发展为了保障航空发动机的安全运转，人们研究出了航空发动机监测技术。
航空发动机监测技术包括航空发动机的在线监测、离线监测和预测维护等多种技术。
其中，离线监测主要包括发动机的开箱检测和分析等手段。
预测维护则是通过对发动机的运转情况进行分析，预测出未来可能出现的问题并进行提前的维护，来保障发动机的运转稳定性。
近年来，航空发动机监测技术得到了广泛的研究和应用。
随着无线通信技术、数据处理能力的不断提升，智能化、自动化的航空维护系统也日益成熟。
通过对传感器、控制系统和通信设备等方面的升级，航空发动机监测技术正在逐渐实现“智能化”。
三、航空发动机监测技术的优势航空发动机监测技术的优势在于可以通过在线采集和分析发动机的运转数据，及时发现运转中存在的安全隐患并进行修复。
同时也可以通过对数据的综合分析，实现对航空发动机的长期健康评估和预测，进而提高维修效率和减少维护成本，有利于减少停机时间和提高飞行安全。
四、航空发动机监测技术的应用目前，航空发动机监测技术的应用已经广泛开展。
在国际市场上，这些技术已经成为了大型民用客机的技术标配，并逐渐得到军用飞机、网络航空运输以及其他一些领域的应用。
而在我国，虽然航空发动机监测技术的研究还处于初级阶段，但是已经开始在一些航空公司和机场得到应用。
航空发动机性能监测系统设计与验证
Internal Combustion Engine &Parts0引言发动机是飞机的重要组成部份，其内部结构复杂，发动机故障在所有飞机机械故障中的比例高到1/3[1]。
依据目前的设计、制造、使用及维修维护水平等都不能保证发动机在使用中不出现故障[1，2]。
为了确保航空发动机的工作安全和飞行安全，需定期开展发动机维护工作，耗费了大量的人力、物力和财力，且常出现故障的漏检，造成大量的飞行事故[3]。
因此，在航空发动机领域提出了健康管理技术的需求[4]。
航空发动机性能监测是健康管理的基础，维修决策是在性能监测的基础上对发动机的拆换与送修做出决策，以便后续发动机机队调度，在保障安全飞行的前提下达到机队发动机的最大利用率[5-7]。
针对航空发动机目前研究状态及需求，本文结合OSA-CBM 系统开放式层次功能，给出了一种机载性能监测系统结构，并设计了通用性的健康管理性能监测软件，具有良好的可扩展性。
1健康管理体系结构视情维修体系结构OSA-CBM 目前已广泛应用于各个领域，包括舰船系统、航空系统，目前已被作为实现健康管理的通用体系架构，被广泛的应用于军民发动机领域。
OSA-CBM 标准的目的是提供一种开放的共享的技术规范用于技术的发展。
该体系框架将OSA-CBM 系统分为7层，包括数据采集与传输层、数据处理层、性能监测层、健康评估层、故障预测层、自动推理决策层、人机接口层，最终实现视情维护，资源调配的目的。
其中数据处理、性能监测、健康评估、故障预测、推理决策是该架构的核心，是实现系统功能的关键。
2航空发动机性能监测系统设计与实现结合CBM 标准的7个层次，对航空发动机机载性能监测系统进行设计，主要研究针对第三层性能监测层，设计了通用的软件，实现机载性能监测系统。
2.1性能监测系统软件通用架构设计航空发动机对机载性能监测系统主要通过对发动机各截面参数的监测，判断当前发动机的状态。
性能监测系统实时采集、处理、分析和记录发动机各截面、分系统相关参数，实现超限报警功能。
航空发动机健康监测与预测技术研究
航空发动机健康监测与预测技术研究近年来，航空业的发展可谓是狂飙突进，而发动机作为飞机的“心脏”，在整个航空过程中扮演着非常重要的角色。
因此，航空发动机健康监测与预测技术的研究逐渐受到关注。
一、背景航空发动机作为飞机的重要组成部分，其性能的安全性和可靠性对航班的成功与否至关重要。
但现实情况是，传统的发动机维护方式往往是基于时间、里程或日历等规则制定计划，并以固定间隔对其进行维修或更换。
这种方式存在许多问题，例如：1.费用较高。
因为它需要定期维护和检查，而这部分费用、人力和设备实际上被闲置，这对航空公司的运营成本产生了较大的压力。
2.延迟检测时间。
由于发动机的检测和维修需要停飞，这会造成航班延误，影响航空公司的运营计划，甚至会影响到旅客的生意和口碑。
3.难以保证发动机的完全健康状况，因为传统方法仅仅是对发动机进行简单的诊断检测。
如果发动机在使用期间不断积累磨损，则可能出现危险事故。
二、航空发动机健康监测与预测技术研究的意义航空发动机健康监测与预测技术研究是在此背景下发展起来的，其主要意义如下：1.实现对发动机健康状态的实时监控，及时发现并解决问题。
2.减少发动机检查和维护过程中的停飞时间，降低维护费用。
3.提高发动机的可靠性和安全性，降低事故风险。
三、航空发动机健康监测与预测技术的方法航空发动机健康监测与预测技术的方法可以分为三类：传感器方法、数据驱动方法和物理模型方法。
1.传感器方法传感器技术是一种常用的方法，它可以直接测量发动机的物理和化学特征，例如振动、温度、压力、声音、气态成分等。
传感器获取的数据可以用于预测发动机的健康状态和提供一些有利于维修的数据。
随着传感器技术的发展，新型的传感器系统也在不断涌现。
2.数据驱动方法数据驱动方法，英文为Data-driven methods，是一种基于数据挖掘和大数据分析的方法。
通过对收集的大量实时数据进行分析，提取出有意义的特征，然后将这些特征与已知的发动机故障数据进行比对，从而得出发动机的健康状况预测。
民航飞机发动机状态监控专业技术与系统研究(终稿)
民航飞机发动机状态监控技术与系统研究指导老师戎翔博士姓名学号朱垒2007011133李毅2007011119周志华2007011111南京航空航天大学金城学院二ОО九年十一月目录1.引言 (3)1.1 概念 (3)1.2 意义 (3)1.3 运用 (3)2.发展历程 (4)2.1早期飞机数据记录器概述 (4)2. 2 飞行数据记录的新发展 (6)3.飞机状态监控技术 (7)3.1 ACMS系统的组成 (7)3.2 QAR概述.............. (7)3.3 ACMS其他部件 (9)3.4 ACMS系统的工作原理 (9)4.发动机状态监控技术 (11)4.1 数据收集 (11)4.2 数据处理 (12)4.3 修正 (13)4.4 警戒 (14)4.5 数据储存 (14)4.6 分析 (14)4.7 展望 (15)4.8 结论 (15)5. 飞机与发动机状态监控系统软件功能介绍 (15)5.1 ACARS (16)5.2 COMPASS (17)5.3 AIRMAN (18)6.总结 (23)7.参考文献 (23)1、引言1.1概念21世纪，随着人类科技的发展，人们的生活变得越来越便捷，地球正变成一个越来越小的地球村。
而现今连接世界各地最方便快捷的途径，就是航空业。
随着民航的日益发展，其快速性当然不言而喻，但是人们正越来越关心的是，民航运输的安全性。
因此，飞机状态监控技术和飞机发动机状态监控技术作为与民航安全性最紧密的部分首先值得我们关注。
最初，人们通过飞行数据记录器，即通常所说的“黑匣子”来记录飞行数据，当飞机发生事故之后，找到飞机飞行数据记录器，根据记录的飞机飞行状态参数，可以分析事故原因。
近年来，随着电子技术、计算机技术和通讯技术的迅速发展，现代民用运输机上开始加装飞行状态监控系统（ACMS），通过系统可以更加有效、及时地利用飞行数据。
对于现代飞机上，飞行数据已经在更多的领域发挥着重要作用：分析飞行数据、为航空公司的“视情维修”提供依据；分析与飞行员飞行操作有关的飞行参数，指导飞行员培训和提高飞行质量；分析飞行数据，总结飞行规律，改进飞机设计；分析试飞中的记录数据，排除故障，消除飞行隐患。
航空发动机状态趋势监控方法
航空发动机状态趋势监控方法1. 引言1.1 背景航空发动机是飞机的心脏，负责提供动力以维持飞行的持续进行。
在飞机飞行的过程中，航空发动机所承受的工作环境极为恶劣，包括高温、高速、高频率的振动等，这些因素会对发动机的性能和状态造成影响。
对航空发动机进行状态监控变得至关重要。
随着航空工业的飞速发展，航空发动机的性能要求越来越高，对于发动机的状态监控也提出了更高的要求。
传统的监控方法往往依赖于定期的检修和检查，无法实时地获取发动机的工作状态信息。
而随着信息技术的发展，基于数据采集、传输和处理的状态监控方法逐渐开始应用于航空发动机领域。
本研究旨在探索一种基于数据采集与处理的航空发动机状态监控方法，通过趋势分析技术和模型建立，实现对发动机状态的实时监控和预测。
这将有助于提高航空发动机的可靠性和安全性，为航空工业的发展提供重要支持。
【背景】1.2 研究意义航空发动机是飞机的核心部件之一，其状态的良好与否直接关系到飞行安全和经济效益。
随着航空业的快速发展，航空发动机的使用频率和强度也越来越高，因此对于航空发动机状态的监控变得尤为重要。
研究航空发动机状态趋势监控方法的意义在于能够及时发现和诊断发动机可能存在的故障和问题，帮助延长发动机的使用寿命，减少维护成本，提高飞行安全性。
通过对航空发动机状态进行有效监控，可以及时发现发动机偏离正常工作状态的迹象，为飞行员和维护人员提供及时的预警信息，避免发生严重事故。
航空发动机状态趋势监控方法的研究还具有推动航空发动机技术进步和提高竞争力的作用。
通过不断改进监控方法和技术，可以使航空发动机更加可靠、高效、安全，提升飞机整体性能，满足不断增长的航空市场需求。
深入研究航空发动机状态趋势监控方法具有重要的实践意义和经济意义。
1.3 研究目的研究目的是为了通过航空发动机状态趋势监控方法，实现对发动机状态的实时监测和预测，提高飞行安全性和效率。
航空发动机是飞机的核心部件，其状态的良好与否直接关系到飞行安全和飞机的性能。
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Aeroengine condition monitoring system design study Design research abstract: the aircraft engine condition monitoring system for a certain type of aircraft engine as the research object, discussed the engine state monitoring system design and implementation, this paper mainly discusses the status monitoring system of the overall design, system software and hardware development and testing and redundancy design, and in the design of engine state monitoring system, introduced the embedded PC / 104 modules.In order to make the state monitoring system has a better scalability and adaptability, the system designed can work in three ways: airborne operation mode, data playback mode and ground test run mode, through these way for automatic monitoring of engine state parameter, for the state of the engine trend analysis, fault diagnosis, and as to provide scientific basis for maintenance.Key words: the engine condition monitoring;PC / 104;Redundant design.0.The introductionAs the power source of the airplane flight, aircraft engine research is a very complex process, the aircraft engine test as the development of the three pillars (calculation, manufacture and test), one of its importance is self-evident, it turns out, mainly consisted of engine test, rather than by calculation and analysis.For thedevelopment of an aircraft engine group, is flat, how we can decide the how developed horizontal engine.For aircraft engine manufacture, condition monitoring is an important examination method of engine performance, by condition monitoring and testing work is to adjust the engine performance and break-in, check the manufacturing or overhaul quality, finally gives the engine factory performance report purpose, therefore the accurate and reliable test method for state monitoring to ensure the quality of the engine has the vital significance.As we all know, the engine vibration and high noise in the process of work and the strong jamming signal, all parameters to the variety of the monitoring system puts forward higher requirements.Especially with the improvement of engine performance, real-time monitoring of the parameters needed for condition monitoring is becoming more and more people continue to explore new testing technology for many years, to adapt to the increasingly development of engine test technology requirements. 1. The condition monitoring system overall designThe condition monitoring system is designed for a certain type of turbofan engine, the engine monitoring system of the input signal is part of a total of 17 analog, 3 road 16 way switch frequency and quantity, including 17 analog, 3 road frequency quantity through the signal conditioning circuit into a standard signal conditioning, tolower place machine;Under 16 way switch quantity after adopting the light into a machine.In the data processing part of the need for continuous monitoring of state parameters of the engine, so in the need for signal processing, storage and transmission, the upper machine at the same time, according to the need to receive the signal in real time and if samples values more than its upper and lower give alarm, including buzzer lower place machine and superordination machine screen alarm; PC the data display mode including curve display, digital display, simulation table display and record the browsing;The data communication between upper machine and lower machine way using serial communication.In order to improve the extensibility and adaptability of engine state monitoring system, the system designed can work in three ways: airborne operation mode, data playback mode and ground test mode, including airborne operation mode can be PC / 104 modules separately in the form of the airborne launch during the real flight test, because of the PC / 104 modules, small volume, high reliability, high temperature resistance and resistance to adverse conditions, well adapted to the flight environment, to the parts of the flight state and parameters in the flight details and accurate acquisition, data processing and at the same time, all the data are stored in the solid plate, in order to perform data playback and analysis of engine performance in thefuture;Data playback mode can be kept in a flight test data sent to the PC via a serial port communication, PC receives the data real-time display, including using digital watch display the data in the form of clear, at the same time also to simulate image, in the form of table of data, and in addition you can also choose to use display mode with the curve will be testing the state parameter of dynamic display, and finally to the received data stored in hard disk;Ground test methods organic combination of the airborne playback mode, operation mode and data will be collected in the process of engine test parameters are processed, then real-time transmitting them via a serial port to the PC [4], PC can display the data in real time again at the same time to save them.In order to improve the reliability of the system, make the system self-check function and redundant Yu Jiegou, according to the system fault conditions to change the modulation circuit or downtime.According to the requirements of aircraft engine test of 20 analog quantity and frequency, and quantity of 16 way switch sampling frequency is set to 20 ms, analog signal processing precision for 12, and all recuperated into 0 ~ 5 V. Synthetically considering the factors, the system's upper machine and lower machine USES the microcomputer, and selects the maturity of the A/D conversion board and digital I/O board to collect signals, and designed the signaldisposal circuit of signal isolation, filtering and amplification processing, transformation into A standard signal.Software used to develop ways to meet the system requirements.According to the working environment temperature, humidity, vibration resistance and anti-interference requirements, choose the PC / 104 bus, and lower place machine and peripheral boards have to choose the PC / 104 module, in order to meet the aircraft airborne equipment requirement for the weight, volume, etc.2．condition monitoring system hardware designBased on the system overall design, the whole monitoring system hardware is shown in figure 1 USES hardware structure classifiers, by A P4 PC and A PC / 104 into A unit, under the lower machine and PC / 104 motherboard, peripheral interface card, and all kinds of circuit, and various peripheral interface card circuit including: 3 PC / 104 A/D conversion board, 2 pieces of PC / 104 digital I/O board, light 1 PC / 104 multi-function acquisition board, terminal board, signal disposal circuit switching circuit and field/testing.PC / 104 motherboards, PC / 104 A/D conversion board and PC / 104 digital light every I/O board implementation of engine simulation signal measure and frequency A/D conversion and collection, as well as to the amount of engine digital signal isolation, and signal data processing of collection, storage and transmission.PC/ 104 multi-function acquisition boards and self-inspection/scene switching circuit is used to implement the system self-checking and site data acquisition of switching, and sending self-checking voltage.Signal conditioning circuit to isolation, filtering, amplifying the signal, they transform into standard signal, and then into a machine.PC are used to implement the data acquisition machine by sending data receiving, storage, display and alarm.3.condition monitoring system software designOf the condition monitoring system software module's goal is to develop a set of applicable to the aircraft engine condition monitoring platform system, it can real-time high pressure in the engine working process speed, throttle Angle, displacement, oil injection nozzle location, the fuel flow rate, vibration of X, Y, atmospheric temperature, T, 5, 7 T temperature, oil temperature, oil temperature, hydraulic temperature, car frame under static pressure, the static pressure, high pressure, fuel oil pressure, oil pressure and hydraulic oil pressure, etc. 17 analog and 3 road, frequency, and 16 way switch for data acquisition, processing, storage, operation, such as communication, status display and alarm software running on the upper machine and lower machine respectively, system specific features include:3. 1 state parameter real-time collection and processingIn the system, complete engine various real-time acquisition of state parameter, and the associated with the state parameter of processing and calculation, and the deviations from the normal range of state parameters on the tag, to analyze the engine working condition.3. 2 real-time status parameters and playback data transmission On the surface of the test mode and online data playback mode, a machine under the PC / 104 collected status parameters via a serial port in real-time transmission to the PC, and save to the database ona PC.3.3 status monitoring and real-time data controls and related to useof drawing tools to simulate the acquisition state parameters of the digital display and the table shows, and draw the time curve so that the observation parameter of the parameters of the dynamic change and trend of the future, which can monitor the working state of the engine at different times, to achieve the purpose of condition monitoring.3. 4 alarm output and prin tWhen the collection to the state of the parameter values deviate from normal range after, through the voice warning color to operating personnel, location, and display alarming information, andall the warning point data can be extracted for display, analysis and printing.4 self-checking system and redundancy design4.1 self-checking system signal disposal system is an important part of general data acquisition system, regulate won't create any problems for digital signal, however, on the choice of design and physical components is difficult to guarantee the analog channel signal input and output equals the absolute, for analog signal data acquisition system of the main source of error is the system error, so the data must be taken into account the error and error correction to improve the accuracy of the data, apparently, error correction is mainly in view of the analog channel is concerned.Signal conditioning part in use after period of time there is a deviation, error characteristics of the analog channel may over time slowly changing, digital channel and analog channel in the system is more, the number of regular manual measurement of the integrity of each channel and channel characteristics is impossible, so a self-checking system is designed to meet this requirement, the system of self-inspection system consists of two functions: testing the correctness of the regulate channel;Calculation error characteristics of the analog channel.The self-checking system regularly or not regularly by the system automatically or artificial trigger switchschematic diagram.The implementation of the self-checking process is as follows: (1) Under the PC / 104 a self-inspection program in the machine through the PC / 104 multi-function acquisition boards to K port the self-check voltage of 5 V, the system enters the self-check state. (2) through the PC / 104 multi-function acquisition boards sent TA and TD ports respectively simulated self-checking voltage and voltage digital testing.(3) through the PC / 104 A/D conversion board and PC / 104 digital I/O board is introspected the signal acquisition.(4) the self-inspection program to analyze self-checking signal data processing.Due to the condition monitoring system's environment is very bad, and the running time of the engine condition monitoring system is generally is very long, so the signal disposal circuit selects high reliability of the components as far as possible, at the same time in order to prevent failure in the operation of the system part caused by failure, you should also use redundant circuits to improve system reliability and accuracy, and in some cases, monitoring system for each signal all the way to increase a same function of redundancy control circuit, in order to increase its reliability.Because of the monitoring system as part of the airborne equipment, therefore should be on the basis of considering the equipment volume up andimprove the reliability of system, the system monitoring data according to the circuit characteristics to consider redundancy control circuit design.In the system of the communist party of China 17 frequency analog and 3 road, which is divided into 6 groups, signals are grouped as shown in table 1, including 17 road analog was divided into 5 groups, 3 road frequency quantity is 1 set alone, each group use the same signal disposal circuit undertake recuperating, therefore, the redundancy of the system circuit design, make full use of the conditions in each group share a redundant signal regulate circuit, namely each group use the same modulation circuit, respectively, using the original control signal in normal operation of circuit, when the set is in any way the signal conditioning circuit failure occurs, can switch to the standby redundancy control circuit, continuing state monitoring, so as to improve the accuracy and reliability of system, due to the signal in each group share a conditioning circuit, and will initially need to increase redundant 20 road son signal disposal circuit module circuit, reduced to simply add no.6 redundancy sub circuit for signal disposal circuit, greatly reducing the regulate the number of sub circuit, and improve the accuracy and reliability of the system. According to the above analysis of the monitoring system for monitoring signal and modulation circuit, design of the nursing childson/redundant circuit switching circuit, the event of a failure regulate circuit and redundant circuit switching process is as follows ：2sin ()2sin x y u t x-=++ （14） W (t) is the output of the hysteresis system.Assuming that w (t) is equal to zero, then the system is not stable, because for any x.2sin ()2sin x y u t x-=++ > 0.During the simulation, using the generalized RBF neural network for modeling of inverse hysteresis model.Input control signal for r = 6.5 sin2.3 t and the output of the neural network inverse model for v (t), as shown in figure 3, the model by the neural network inverse hysteresis system under the action of single output for 1 w (t), as shown in figure 4.As can be seen from the figure 3 and figure 4, greatly weakened, hysteresis andhysteresis phenomenon basically eliminated.Parameters of PID controller is: p = 70 K, K (I) = 0.005 K d = 0.Bang - Bang control rule is: A = 1.5, K B = 50, Sp and Sp 1 2 are 0.03 and 0.02 respectively.The figure 5 is not plus Bang - Bang control simulation results, namely for the NPID control, figure 6, 7, 8, and Bang - Bang control after NBPID control simulation results. 4 conclusion Has good characteristics through the use of generalized RBF neural network, direct inverse model of hysteresis system modeling, and then use the inverse model of the implementation of feedforwardcontrol, can greatly reduce the hysteresis phenomenon.By joining Bang - Bang control, can effectively control error.As can be seen from the simulation results, based on feedforward control plus Bang - Bang control of PID control, the hysteresis system can be effectively controlled.This method can also be extended to other types of hysteresis control system.References:[ 1] Hamdan M, Gao Z Q. A novel PID controller fo r pneumat icproportio nal valv es with hyster esis [ J ] . IEEE, 2000, 2:1198- 1201. [ 2] Zhao Hongwei, etc. Piezoelectric ceramic actuator in the application of flexible manipulator robot research [J]. Journal of piezoelectric and acoustics, 2000, 22 (3) : 173-176.[ 3] Choi G S, Kim H S, Cho i G H. A study on position controlof piezoelectric acuators [ A] . ISIE’ 97 [ C] . Portug al,1997.[ 4] Tao G, Kokotovic P V. Adaptive contr ol of plants w ith unknow n hysteresis [ J] . IEEE Tr ans. Autom. Contr. 1995,40( 2) : 200- 212.[ 5] SU C Y, Stepanenko Y, Svoboda J, et al. Robust adaptive contro l of a class of nonlinear systems [ J] . IEEE T ran. Au.to. Con. 2000, 45( 12) : 2427- 2432.[ 6] Cruz- Her nndez J M, Hayward V. Phase control approach to hyster esis reduction [ J] . IEEE Tran. on Contr . Sys.Tech. 2001, 9( 1) : 17- 26. [ 7] Hwang C L, Jan C, Chen Y H. Piezomechanics using intelligent variable- structure control [ J] . IEEE Tran. o n Industrial Electo nics. 2001, 48( 1) : 47- 59.[ 8] Han J M. T. A. Adriaens. Willem L. de Koning , ReinderBanning. Modeling piezo electric actuators [ J ] . IEEE/ASME Tran. Mech. 2000, 5( 4) : 331- 341.[ 9]wang yong ji stuff. Neural network control [M]. Machinery industry press,[ 10] Hay kin S. Neur al Networks[ M] . Prentice- Hall I nc. 1999.航空发动机状态监测系统设计研究康文雄、李华聪、杨勇柯( 1. 华南理工大学, 广东广州510640; 2. 西北工业大学, 陕西西安710072)摘要: 以某型航空发动机为研究对象, 讨论了发动机状态监测系统的设计和实现, 主要探讨了状态监测系统的总体设计, 软硬件开发及自检系统和冗余设计, 并在发动机状态监测系统的设计中引入了嵌入式PC/ 104 模块。