压电传感元件在智能结构中的应用研究

研究了压电材料作为传感元件在智能结构中的应用。开发了基于锆钛酸铅压电陶瓷传感元件的智能结构实验系统，利用压电传感器对复合材料板结构的载荷变化进行监测并对传感器组的冲击定位功能进行了试验。文中采用模式识别技术，成功地实现了结构连接损伤出现的判别和损伤位置的确定     

某型飞机作动器健康现状的评判

通过结合层次分析法和专家经验综合考虑其各影响因素的权重,并通过建立模糊综合评判矩阵对某型飞机作动器作出综合性、定量的评价,由定性评价转化为客观公正的定量评价.根据评判结果对飞机作动器的健康现状作出正确的判断,并对进一步的检修提供建议.     

Beacon Monitoring: Reducing the Cost of Nominal Spacecraft Operations

Beacon monitoring is an architecture for augmenting on-board health assessment software with the following elements: a transmitter that periodically broadcasts this health assessment to the ground, a network of globally distributed low-cost monitoring stations that relays the health assessment to mission control, and an automated mission control system for notifying on-call operators and initiating appropriate response actions. While beacon monitoring is often cited as a means of lowering nominal monitoring costs for particular missions, these claims have typically been qualitative and undiscriminating in nature. This study introduces modeling and experimentation as means of providing a more fundamental validation of the cost-effectiveness of beacon monitoring. Results include simple quantitative estimates of first-order performance metrics, experimental data that verifies predicted performance and validates the use of beacon monitoring for a test mission, and a method for assessing the value of beacon monitoring for general satellites missions.     

飞机单机寿命监控技术评述

单机寿命监控对挖掘每一架飞机的寿命潜力,保证飞机结构的使用安全,延长飞机服役期限具有重要的意义。飞机单机寿命监控从技术方式上分为四种:定期检查和维修,飞参数据监控,危险部位应变监控与飞参数据监控相结合,以智能材料为基础和结构的监控。首先回顾了飞机寿命监控的时间发展历程,并对每个阶段监控方式的优缺点进行了比较,表明随着硬件技术的进步监控方式在不断的进步,精度也越来越高;然后总结了在单机寿命监控发展过程中使用的四种监控技术方式及其优缺点,通过对几种技术方式的分析,认为依赖于智能材料的飞机健康监测系统的监控将是未来单机寿命监控的发展方向。     

船舶主推进系统故障预测与健康管理设计

随着装备系统复杂性、综合化、智能化程度的不断增加,其可靠性、维修性、测试性,保障性、安全性以及寿命周期费用的问题越来越受到人们的重视,为故障预测与健康管理(PHM)技术的产生发展提供了契机。本文首先简要介绍了PHM的内涵、功能及结构。在此基础上,以船舶主推进系统为研究对象,从体系结构、应用功能组成两个方面开展船舶主推进系统的PHM初步设计。最后,重点针对其中的设备健康检查模块,从工作原理、模块组成及功能实现流程等方面进行了详细设计。     

复合材料飞机结构健康监测系统的若干问题探讨

针对飞机复合材料结构状态监测技术,从结构健康监测系统、结构健康监测研究对象和损伤识别方法,以及复合材料飞机的结构设计和结构健康监测系统三方面之间的关系进行阐述。分析了复合材料飞机结构健康监测系统的发展趋势,并讨论了制约该技术发展的因素。     

飞机预测与健康管理体系结构浅析

飞机预测与健康管理(Prognostics and Health Management,PHM)是以美国JSF飞机为代表的新一代飞机自主后勤概念的核心.PHM技术的应用,有效地降低了飞机全寿命周期的使用维护费用,提高了飞机战备完好率和任务成功率,保证了飞机及其各系统能够安全、可靠、高效地运行.本文在分析了美国JSF飞机...     

试论飞行学生飞行训练阶段心理健康教育

飞行学生良好心理素质的养成,除了在入校筛选中进行严格挑选之外,就是要做好对飞行学生的心理健康教育工作,尤其要注重对飞行学生进行系统、持续、全面的心理健康教育,为此,飞行学生在飞行训练学习阶段的心理健康教育工作就显得更加重要.     

A novel approach of testability modeling and analysis for PHM systems based on failure evolution mechanism

Prognostics and health management (PHM) significantly improves system availability and reliability, and reduces the cost of system operations. Design for testability (DFT) developed concurrently with system design is an important way to improve PHM capability. Testability modeling and analysis are the foundation of DFT. This paper proposes a novel approach of testability modeling and analysis based on failure evolution mechanisms. At the component level, the fault progression-related information of each unit under test (UUT) in a system is obtained by means of failure modes, evolution mechanisms, effects and criticality analysis (FMEMECA), and then the failure-symptom dependency can be generated. At the system level, the dynamic attributes of UUTs are assigned by using the bond graph methodology, and then the symptom-test dependency can be obtained by means of the functional flow method. Based on the failure-symptom and symptom-test dependencies, testability analysis for PHM systems can be realized. A shunt motor is used to verify the application of the approach proposed in this paper. Experimental results show that this approach is able to be applied to testability modeling and analysis for PHM systems very well, and the analysis results can provide a guide for engineers to design for testability in order to improve PHM performance.     

一种可用于航空发动机健康状态预测的动态集成极端学习机模型

提出一种动态集成极端学习机模型用于航空发动机健康状态预测.采用AdaBoost.RT集成学习算法对极端学习机（ELM）进行集成,在训练时采用每个训练样本的近邻样本对ELM的局域性能进行评估;在预测时首先确定新样本在训练样本集中的近邻样本,然后根据ELM在近邻样本上的性能来赋予集成权值实现弱学习机的动态集成.以燃油流量为指标进行航空发动机健康状态预测,动态集成ELM模型短期预测结果的平均相对误差绝对值（MAPE）为3.688%,小于单一ELM模型的3.830%以及静态集成ELM模型的3.719%;长期预测结果中动态集成ELM模型的MAPE为3.075%,小于单一ELM模型的4.355%以及静态集成ELM模型的3.884%.因此动态集成ELM模型更适用于航空发动机健康状态预测.