PHM技术在液压伺服系统中的应用

为了提高液压伺服系统的可靠性和可用性,将故障预测与健康管理（PHM）技术应用到该系统中,并对其核心技术进行研究。重点研究在液压伺服系统的性能发生退化时对该系统进行的故障检测、健康评估及预测。对液压伺服系统PHM技术研究可为系统故障检测、退化评估和寿命预测的工程实践提供有力的理论支撑,并且PHM技术研究是故障诊断技术新的发展方向。最终,实验结果证明了该PHM技术在液压伺服系统故障诊断上的有效性和适用性。     

无人机故障预测与健康管理系统研究

PHM(故障预测与健康管理)技术作为一种新兴的故障诊断与管理方法,能对无人机重要部件、系统进行全面的健康状态监控、故障检测、分析判断处理,因而在无人机技术领域得到广泛重视和长足发展.文章通过对PHM技术的分析研究,搭建了无人机故障预测与健康管理系统的总体框架,并设计了无人机故障预测与健康管理系统模块.     

民用飞机系统的故障预测与健康管理系统设计

简要阐述了故障预测与健康管理（PHM）的内涵以及主要功能．提出了关于民用飞机系统的PHM系统的主要功能模块．包括数据分析、故障诊断、健康评估、寿命预测和维修决策等,以此设计了系统结构模块,并对其进行了详细介绍,最后提出了飞机系统PHM系统的设计与实现时的核心技术以及注意事项。     

PHM系统验证指标研究

在分析预测与健康管理(PHM)核查与验证技术发展现状的基础上,深入分析了PHM系统核查与验证指标,从故障诊断和故障预测两个方面对主要的PHM系统能力度量指标进行定义和诠释。     

故障预测与健康管理体系结构综述

随着装备系统复杂化、综合化、智能化、自动化、精密化的不断发展提高,其可靠性、维修性、测试保障性、安全性以及全寿命周期管理的问题越来越受到重视,传统的事后故障维修诊断不利于装备的维修和后勤保障,因而故障预测与健康管理(PHM)技术应用而生.本文阐述PHM系统框架,明确不同PHM体系下的工作流程,实例分析了PHM体系结构的...     

多传感器信息融合技术与无人机PHM系统

通过分析多传感器数据融合技术故障诊断方法,针对无人机的特点,在不增加系统硬件的情况下,充分利用无人机现有传感器获取的信号,提高无人机故障预测与健康管理(PHM)系统状态监测、健康评估和故障预测推理的准确性,并确定推理结果的置信度.     

航空电子的故障预测与健康管理技术

分析了引发航空电子设备故障的各种因素;详细讨论了航空电子设备全寿命周期内的故障预测与健康管理（PHM）技术,包括故障检测与健康监测、健康信息处理、故障预测和余寿评估;还介绍了基于健康信息综合管理的航空电子PHM系统的设计。     

民用飞机健康管理标准规范体系分析

现代民用飞机在设计和维修领域的故障预测与健康管理(PHM)系统研制均需要技术标准的支撑,随着PHM技术的快速发展和应用,提高PHM系统研制的标准化水平,也成为提高民机健康管理业务灵活性与效率、技术风险的科学评估与管理、投资回报水平的迫切需要。全面梳理分析了以SAE、MIMOSA、ARINC、FAA、RTCA、IEEE为代表的国外先进标准组织编制的PHM相关的标准体系和应用范围;指出国内民用飞机故障预测与健康管理标准研究、编制和应用差距;参考SAE HM-1标准规范体系的规划和建设思路,基于系统工程的方法论,从顶层进行规划,初步设计了我国民机健康管理标准体系框架和各专题的标准题目分解。最后提出我国PHM标准体系开发思路,以及标准体系的编制工作实施建议。     

大型飞机机载系统预测与健康管理关键技术

为确保飞机的高可靠性、高安全性和高维修保障性,大型飞机机载系统均装备了先进的故障预测与健康管理(PHM)系统,以实现高可靠运行和健康服役。从大型飞机健康管理体系结构入手,介绍了基于国际标准分层开放式的故障预测与健康管理空地结构,及其开放式、模块化和标准接口规范。着重分析了机载健康管理传感器网络和鲁棒故障特征提取方法、分层聚类和交叉增强校核的智能故障诊断算法和基于数据驱动与失效物理结合的故障预测算法等关键技术,探讨了基于健康状态的维修保障决策方法。最后,给出了空地一体的飞机故障预测与健康管理评价方法和健康管理技术的适用性分析。     

飞机健康管理标准研究综述

近年来故障预测与健康管理(Prognostics and Health Management,PHM)技术快速发展,已逐渐迈向工程应用阶段,然而国内飞机健康管理标准研究基础薄弱,对健康管理系统设计开发、验证评价等工作缺乏指导和约束.针对此问题,本文全面梳理分析了SAE、IEEE、ISO、MIMOSA、ARINC等国际标...     

军用航空发动机PHM发展策略及关键技术

提出了军用航空发动机预测与健康管理(prognostics and health management,简称PHM)系统的发展策略和关键技术.首先回顾了国外航空发动机PHM技术的发展历史,简要分析了各个阶段具有代表性的技术特点;其次,详细论述了航空发动机PHM技术发展应当妥善处理的关系和重要问题,主要包括PHM技术发展与空军军事需求、PHM技术发展与技术成熟度体系、立足三代机平台发展PHM技术、在发动机全寿命管理体系引入PHM系统、建议的航空发动机PHM功能和结构等;接着,结合国内技术发展水平,给出了应当重点优先发展关键技术的建议;最后,简要总结了制约国内PHM技术发展的因素,展望了瞄准的技术发展目标.      

IEEE Standards for Prognostics and Health Management

Recently operators of complex systems such as aircraft, power plants, and networks have been emphasizing the need for on-line health monitoring for purposes of maximizing operational availability and safety. The discipline of prognostics and health management (PHM) is being formalized to address the information management and prediction requirements for addressing these needs. Herein, we will explore how standards currently under development within the IEEE can be used to support PHM applications. Particular emphasis will be placed on the role of PHM and PHM-related standards with Department of Defense (DOD) automatic test systems-related research.     

基于特征参数趋势进化的故障诊断和预测方法

 采用时间序列方法对可以表征系统故障状态的特征参数的趋势进化进行预测,同时考虑特征参数的概率分布特性,给出了对系统进行故障诊断和预测的方法。在已获得特征参数监测数据的基础上,分别对具有广义强度/故障阈值确定分布或故障模式特征参数空间分布两种形式的故障判据,提出了利用二次指数平滑预测模型对系统未来某时刻的故障状态进行预测的方法。给出包括故障概率和故障指数在内的故障诊断和预测结果形式,可进一步为系统的维修决策等提供参考。     

民用飞机健康管理技术研究

随着民用飞机的系统集成度和复杂性大大提高,为降低民用飞机的运营成本、提高安全性和可靠性,民用飞机健康管理系统成为航空制造行业的发展趋势。本文简要介绍了民用飞机健康管理技术在国内外的发展现状,现阶段技术条件下所面临的问题以及所涉及的关键技术。着重介绍了民用飞机健康管理技术研究的方法和研究的内容,并对民用飞机健康管理技术未来的发展趋势进行了展望。     

Aircraft air conditioning system health state estimation and prediction for predictive maintenance

The vast potential of system health monitoring and condition based maintenance on modern commercial aircraft is being realized through the innovative use of Airplane Condition Monitoring System (ACMS) data. However there are few methods addressing the issues of failure prognostics and predictive maintenance for commercial aircraft Air Conditioning System (ACS). This study developed a Bayesian failure prognostics approach using ACMS data for predictive maintenance of ACS. First, a health index characterizing the ACS health state is inferred from a multiple sensor signals using a data driven method. Then a dynamic linear model is proposed to describe the degradation process for failure prognostics. Bayesian inference formulas are carried out for degradation estimation and prediction. The developed approach is applied on a passenger aircraft fleet with ACMS data recorded for one year. The analysis of the case study shows that the developed method can produce satisfactory prognostics results, where all the ACS failure precursors are identified in advance, and the relative errors for the failure time prediction made when just entering the degradation warning stage are less than 8%. This would allow operators to proactively plan future maintenance.     

Remaining useful life prognostics for aeroengine based on superstatistics and information fusion

Remaining useful life（RUL） prognostics is a fundamental premise to perform conditionbased maintenance（CBM） for a system subject to performance degradation. Over the past decades,research has been conducted in RUL prognostics for aeroengine. However, most of the prognostics technologies and methods simply base on single parameter, making it hard to demonstrate the specific characteristics of its degradation. To solve such problems, this paper proposes a novel approach to predict RUL by means of superstatistics and information fusion. The performance degradation evolution of the engine is modeled by fusing multiple monitoring parameters, which manifest non-stationary characteristics while degrading. With the obtained degradation curve,prognostics model can be established by state-space method, and then RUL can be estimated when the time-varying parameters of the model are predicted and updated through Kalman filtering algorithm. By this method, the non-stationary degradation of each parameter is represented, and multiple monitoring parameters are incorporated, both contributing to the final prognostics. A case study shows that this approach enables satisfactory prediction evolution and achieves a markedly better prognosis of RUL.     

Sensor Optimization Selection Model Based on Testability Constraint

Sensor selection and optimization is one of the important parts in design for testability. To address the problems that the traditional sensor optimization selection model does not take the requirements of prognostics and health management especially fault prognostics for testability into account and does not consider the impacts of sensor actual attributes on fault detectability, a novel sensor optimization selection model is proposed. Firstly, a universal architecture for sensor selection and optimization is provided. Secondly, a new testability index named fault predictable rate is defined to describe fault prognostics requirements for testability. Thirdly, a sensor selection and optimization model for prognostics and health management is constructed, which takes sensor cost as objective function and the defined testability indexes as constraint conditions. Due to NP-hard property of the model, a generic algorithm is designed to obtain the optimal solution. At last, a case study is presented to demonstrate the sensor selection approach for a stable tracking servo platform. The application results and comparison analysis show the proposed model and algorithm are effective and feasible. This approach can be used to select sensors for prognostics and health management of any system.