Health management permits the reliability of a system and plays a increasingly important role for achieving efficient system-level maintenance.It has been used for remaining useful life（RUL） prognostics of electroni...Health management permits the reliability of a system and plays a increasingly important role for achieving efficient system-level maintenance.It has been used for remaining useful life（RUL） prognostics of electronics-rich system including avionics.Prognostics and health management（PHM） have become highly desirable to provide avionics with system level health management.This paper presents a health management and fusion prognostic model for avionics system,combining three baseline prognostic approaches that are model-based,data-driven and knowledge-based approaches,and integrates merits as well as eliminates some limitations of each single approach to achieve fusion prognostics and improved prognostic performance of RUL estimation.A fusion model built upon an optimal linear combination forecast model is then utilized to fuse single prognostic algorithm representing the three baseline approaches correspondingly,and the presented case study shows that the fusion prognostics can provide RUL estimation more accurate and more robust than either algorithm alone.展开更多
Current research on Digital Twin(DT)based Prognostics and Health Management(PHM)focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring ...Current research on Digital Twin(DT)based Prognostics and Health Management(PHM)focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring and health management.However,there still exist gaps in the seamless integration of DT and PHM,as well as in the development of DT multi-field coupling modeling and its dynamic update mechanism.When the product experiences long-period degradation under load spectrum,it is challenging to describe the dynamic evolution of the health status and degradation progression accurately.In addition,DT update algorithms are difficult to be integrated simultaneously by current methods.This paper proposes an innovative dual loop DT based PHM framework,in which the first loop establishes the basic dynamic DT with multi-filed coupling,and the second loop implements the PHM and the abnormal detection to provide the interaction between the dual loops through updating mechanism.The proposed method pays attention to the internal state changes with degradation and interactive mapping with dynamic parameter updating.Furthermore,the Independence Principle for the abnormal detection is proposed to refine the theory of DT.Events at the first loop focus on accurate modeling of multi-field coupling,while the events at the second loop focus on real-time occurrence of anomalies and the product degradation trend.The interaction and collaboration between different loop models are also discussed.Finally,the Permanent Magnet Synchronous Motor(PMSM)is used to verify the proposed method.The results show that the modeling method proposed can accurately track the lifecycle performance changes of the entity and carry out remaining life prediction and health management effectively.展开更多
Condition monitoring is increasingly used to anticipate and detect failures of industrial machines.Failures of machines can cause high maintenance or replacement costs.If neglected,it may result in catastrophic accide...Condition monitoring is increasingly used to anticipate and detect failures of industrial machines.Failures of machines can cause high maintenance or replacement costs.If neglected,it may result in catastrophic accidents leading to production shrinkage.The potential failure would negatively affect the profitability of the company,including production shut down,cost of spare parts,cost of labor,damage of reputation,risk of injury to people and the environment.In recent years,condition-based maintenance( CBM) and prognostic and health management( PHM) are developed and formed a strong connection among science,engineering,computer,reliability,communication,management,etc.Computerized maintenance management systems( CMMS) store a lot of data regarding the fault diagnosis and life prediction of the machinery equipment.It's too necessary to uncover useful knowledge from the huge amount of data.It's vital to find the ways to obtain useful and concise information from these data.This information can be of great influence in the decision making of managers.This article is a review of intelligent approaches in machinery faults diagnosis and prediction based on PHM and CBM.展开更多
故障预测与健康管理(Prognostic and Health Management,PHM)采用智慧运维方式实时监测公共交通的运行状态,挖掘公共交通设备和车辆运行数据,实现故障预测、按需检修、精准维护的功能。利用PHM技术实现对“公共交通健康”的科学管理,能...故障预测与健康管理(Prognostic and Health Management,PHM)采用智慧运维方式实时监测公共交通的运行状态,挖掘公共交通设备和车辆运行数据,实现故障预测、按需检修、精准维护的功能。利用PHM技术实现对“公共交通健康”的科学管理,能够提高公共交通设备的可靠性,降低故障发生率,提升运营效率和经济效益,在公共交通管理中具有较好的实用价值。展开更多
PHM(prognostics and health management)系统健康评估作为连接故障诊断和故障预测的纽带,具有十分重要的作用。针对自动驾驶仪PHM系统健康评估存在的多工况、非线性和小子样问题,提出了一种面向自动驾驶仪PHM系统的贝叶斯网络简化推理...PHM(prognostics and health management)系统健康评估作为连接故障诊断和故障预测的纽带,具有十分重要的作用。针对自动驾驶仪PHM系统健康评估存在的多工况、非线性和小子样问题,提出了一种面向自动驾驶仪PHM系统的贝叶斯网络简化推理模型,建立了一种基于可变信息的改进贝叶斯节点和迭代交叉熵测度的变模型快速推理算法。算法理论仿真实验验证了其有效性和工程可行性,最后在某型直升机电动舵机平台上验证了该改进算法,具有较好的工程应用前景。展开更多
面向电子设备故障预测与健康管理(Prognostics and Health Management,PHM),基于自适应谱峭度与核概率距离聚类提出一种振动载荷下板级封装潜在故障特征提取与模式辨识方法.首先,基于最大谱峭度原则利用经验模态分解的方法对电子组件的...面向电子设备故障预测与健康管理(Prognostics and Health Management,PHM),基于自适应谱峭度与核概率距离聚类提出一种振动载荷下板级封装潜在故障特征提取与模式辨识方法.首先,基于最大谱峭度原则利用经验模态分解的方法对电子组件的应变响应数据进行滤波,计算并重构包含潜在故障信息的包络谱形成故障征兆向量;其次,应用高斯径向基核函数概率距离方法,将非线性故障征兆数据映射到高维Hilbert空间,对其进行聚类分析形成表征板级封装健康状态与各故障模式的类中心;最后,根据实时监测的板级封装的包络谱数据计算与各中心的概率距离,判断其所属的状态从而实现对封装故障模式的早期辨识.通过试验分析,该方法可以有效辨识与预测板级封装即将发生的故障模式,为实现电子设备PHM提供了一种新式的思路与手段.展开更多
对已有的航空装备可用度分析方法进行了综述,将航空装备可用度影响因素分为三类.通过PHM(Prognostics and Health Management)技术对航空装备保障过程的影响分析,梳理了基于PHM的航空装备可用度影响因素,并将这些因素按照其来源分为使...对已有的航空装备可用度分析方法进行了综述,将航空装备可用度影响因素分为三类.通过PHM(Prognostics and Health Management)技术对航空装备保障过程的影响分析,梳理了基于PHM的航空装备可用度影响因素,并将这些因素按照其来源分为使用影响因素和设计影响因素两大类.给出了4个合理的仿真假设条件以简化仿真过程,并进一步将设计影响因素分为5种,并结合一个实例给出了基于PHM的航空装备可用度影响因素仿真过程中的输入.提出给定装备系统及其保障系统参数条件下的稳态可用度为仿真的输出,并给出与仿真输入相符的解析计算公式.详细描述了仿真程序的构成及其流程图.最后,以图形的方式给出了仿真结果,给出了案例的单因素分析及多因素耦合分析的过程及结论,验证了所提出的分析方法的可行性.展开更多
为克服传统维修保障方式的缺陷并适应现代雷达装备维修保障的发展需求，引用当前国外装备维修保障最新技术——故障预测与健康管理（prognostics and health management，PHM），构建了基于PHM的雷达装备维修保障系统，并对主要模块进...为克服传统维修保障方式的缺陷并适应现代雷达装备维修保障的发展需求，引用当前国外装备维修保障最新技术——故障预测与健康管理（prognostics and health management，PHM），构建了基于PHM的雷达装备维修保障系统，并对主要模块进行了研究和分析。该系统能极大提高雷达装备故障诊断能力，有效降低保障费用，并可对装备未来的状况和剩余寿命进行有效预测。展开更多
文摘Health management permits the reliability of a system and plays a increasingly important role for achieving efficient system-level maintenance.It has been used for remaining useful life（RUL） prognostics of electronics-rich system including avionics.Prognostics and health management（PHM） have become highly desirable to provide avionics with system level health management.This paper presents a health management and fusion prognostic model for avionics system,combining three baseline prognostic approaches that are model-based,data-driven and knowledge-based approaches,and integrates merits as well as eliminates some limitations of each single approach to achieve fusion prognostics and improved prognostic performance of RUL estimation.A fusion model built upon an optimal linear combination forecast model is then utilized to fuse single prognostic algorithm representing the three baseline approaches correspondingly,and the presented case study shows that the fusion prognostics can provide RUL estimation more accurate and more robust than either algorithm alone.
基金co-supported by the National Natural Science Foundation of China(Nos.U223321251875014)+1 种基金the Beijing Natural Science Foundation,China(No.L221008)the China Scholarship Council(No.202106020001).
文摘Current research on Digital Twin(DT)based Prognostics and Health Management(PHM)focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring and health management.However,there still exist gaps in the seamless integration of DT and PHM,as well as in the development of DT multi-field coupling modeling and its dynamic update mechanism.When the product experiences long-period degradation under load spectrum,it is challenging to describe the dynamic evolution of the health status and degradation progression accurately.In addition,DT update algorithms are difficult to be integrated simultaneously by current methods.This paper proposes an innovative dual loop DT based PHM framework,in which the first loop establishes the basic dynamic DT with multi-filed coupling,and the second loop implements the PHM and the abnormal detection to provide the interaction between the dual loops through updating mechanism.The proposed method pays attention to the internal state changes with degradation and interactive mapping with dynamic parameter updating.Furthermore,the Independence Principle for the abnormal detection is proposed to refine the theory of DT.Events at the first loop focus on accurate modeling of multi-field coupling,while the events at the second loop focus on real-time occurrence of anomalies and the product degradation trend.The interaction and collaboration between different loop models are also discussed.Finally,the Permanent Magnet Synchronous Motor(PMSM)is used to verify the proposed method.The results show that the modeling method proposed can accurately track the lifecycle performance changes of the entity and carry out remaining life prediction and health management effectively.
基金Fundamental Research Funds for the Central Universities,China(No.DUT17GF214)
文摘Condition monitoring is increasingly used to anticipate and detect failures of industrial machines.Failures of machines can cause high maintenance or replacement costs.If neglected,it may result in catastrophic accidents leading to production shrinkage.The potential failure would negatively affect the profitability of the company,including production shut down,cost of spare parts,cost of labor,damage of reputation,risk of injury to people and the environment.In recent years,condition-based maintenance( CBM) and prognostic and health management( PHM) are developed and formed a strong connection among science,engineering,computer,reliability,communication,management,etc.Computerized maintenance management systems( CMMS) store a lot of data regarding the fault diagnosis and life prediction of the machinery equipment.It's too necessary to uncover useful knowledge from the huge amount of data.It's vital to find the ways to obtain useful and concise information from these data.This information can be of great influence in the decision making of managers.This article is a review of intelligent approaches in machinery faults diagnosis and prediction based on PHM and CBM.
文摘故障预测与健康管理(Prognostic and Health Management,PHM)采用智慧运维方式实时监测公共交通的运行状态,挖掘公共交通设备和车辆运行数据,实现故障预测、按需检修、精准维护的功能。利用PHM技术实现对“公共交通健康”的科学管理,能够提高公共交通设备的可靠性,降低故障发生率,提升运营效率和经济效益,在公共交通管理中具有较好的实用价值。
文摘PHM(prognostics and health management)系统健康评估作为连接故障诊断和故障预测的纽带,具有十分重要的作用。针对自动驾驶仪PHM系统健康评估存在的多工况、非线性和小子样问题,提出了一种面向自动驾驶仪PHM系统的贝叶斯网络简化推理模型,建立了一种基于可变信息的改进贝叶斯节点和迭代交叉熵测度的变模型快速推理算法。算法理论仿真实验验证了其有效性和工程可行性,最后在某型直升机电动舵机平台上验证了该改进算法,具有较好的工程应用前景。
文摘面向电子设备故障预测与健康管理(Prognostics and Health Management,PHM),基于自适应谱峭度与核概率距离聚类提出一种振动载荷下板级封装潜在故障特征提取与模式辨识方法.首先,基于最大谱峭度原则利用经验模态分解的方法对电子组件的应变响应数据进行滤波,计算并重构包含潜在故障信息的包络谱形成故障征兆向量;其次,应用高斯径向基核函数概率距离方法,将非线性故障征兆数据映射到高维Hilbert空间,对其进行聚类分析形成表征板级封装健康状态与各故障模式的类中心;最后,根据实时监测的板级封装的包络谱数据计算与各中心的概率距离,判断其所属的状态从而实现对封装故障模式的早期辨识.通过试验分析,该方法可以有效辨识与预测板级封装即将发生的故障模式,为实现电子设备PHM提供了一种新式的思路与手段.
文摘对已有的航空装备可用度分析方法进行了综述,将航空装备可用度影响因素分为三类.通过PHM(Prognostics and Health Management)技术对航空装备保障过程的影响分析,梳理了基于PHM的航空装备可用度影响因素,并将这些因素按照其来源分为使用影响因素和设计影响因素两大类.给出了4个合理的仿真假设条件以简化仿真过程,并进一步将设计影响因素分为5种,并结合一个实例给出了基于PHM的航空装备可用度影响因素仿真过程中的输入.提出给定装备系统及其保障系统参数条件下的稳态可用度为仿真的输出,并给出与仿真输入相符的解析计算公式.详细描述了仿真程序的构成及其流程图.最后,以图形的方式给出了仿真结果,给出了案例的单因素分析及多因素耦合分析的过程及结论,验证了所提出的分析方法的可行性.
文摘为克服传统维修保障方式的缺陷并适应现代雷达装备维修保障的发展需求，引用当前国外装备维修保障最新技术——故障预测与健康管理（prognostics and health management，PHM），构建了基于PHM的雷达装备维修保障系统，并对主要模块进行了研究和分析。该系统能极大提高雷达装备故障诊断能力，有效降低保障费用，并可对装备未来的状况和剩余寿命进行有效预测。
