Health management based on fusion prognostics for avionics systems

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.

Overview of the Importance of Intelligent Approaches on Machinery Faults Diagnosis and Prediction Based on Prognostic and Health Management/Condition-Based Maintenance

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.

A Certifiable Framework for Health Monitoring and Management

The scope of this paper is to provide an E2 Eperspective of health monitoring and management(HMM)and structural health mornitoring(SHM)as an integrated system element of an integrated system health monitoring and management(ISHM)system.The paper will address two main topics:(1)The importance of a diagnostics and prognostic requirements specification to develop an innovative health monitoring and management system;(2)The certification of a health monitoring and management system aiming at a maintenance credit as an integral part of the maintenance strategies.The development of a maintenance program which is based on combinations of different types of strategies(preventive,condition-based maintenance(CBM)and corrective maintenance…)for different subsystems or components and structures of complex systems like an aircraft to achieve the most optimized solution in terms of availability,cost and safety/certification is a real challenge.The maintenance strategy must satisfy the technical-risk and cost feasibility of the maintenance program.

Dynamically updated digital twin for prognostics and health management:Application in permanent magnet synchronous motor

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.

Test selection and optimization for PHM based on failure evolution mechanism model

The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability （DFT） to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propa- gation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model （FEMM） for PHM systems is built up. This model describes the fault evolution- test dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of in- herent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and mini- mize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm （ASAGA） is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.

Large Power Transformer Fault Diagnosis and Prognostic Based on DBNC and D-S Evidence Theory

Power transformer is a core equipment of power system, which undertakes the important functions of power transmission and transformation, and its safe and stable operation has great significance to the normal operation of the whole power system. Due to the complex structure of the transformer, the use of single information for condition-based maintenance (CBM) has certain limitations, with the help of advanced sensor monitoring and information fusion technology, multi-source information is applied to the prognostic and health management (PHM) of power transformer, which is an important way to realize the CBM of power transformer. This paper presents a method which combine deep belief network classifier (DBNC) and D-S evidence theory, and it is applied to the PHM of the large power transformer. The experimental results show that the proposed method has a high correct rate of fault diagnosis for the power transformer with a large number of multi-source data.

An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development

Digital technologies are becoming more pervasive and industrial companies are exploiting them to enhance the potentialities related to Prognostics and Health Management(PHM).Indeed,PHM allows to evaluate the health state of the physical assets as well as to predict their future behaviour.To be effective in developing PHM programs,the most critical assets should be identified so to direct modelling efforts.Several techniques could be adopted to evaluate asset criticality;in industrial practice,criticality analysis is amongst the most utilised.Despite the advancement of artificial intelligence for data analysis and predictions,the criticality analysis,which is built upon both quantitative and qualitative data,has not been improved accordingly.It is the goal of this work to propose an ontological formalisation of a multi-attribute criticality analysis in order to i)fix the semantics behind the terms involved in the analysis,ii)standardize and uniform the way criticality analysis is performed,and iii)take advantage of the reasoning capabilities to automatically evaluate asset criticality and associate a suitable maintenance strategy.The developed ontology,called MOCA,is tested in a food company featuring a global footprint.The application shows that MOCA can accomplish the prefixed goals;specifically,high priority assets towards which direct PHM programs are identified.In the long run,ontologies could serve as a unique knowledge base that integrate multiple data and information across facilities in a consistent way.As such,they will enable advanced analytics to take place,allowing to move towards cognitive Cyber Physical Systems that enhance business performance for companies spread worldwide.

基于监督机器学习算法的动车组PHM预警核验方法研究

为提高故障预测与健康管理PHM模型预警的准确率以有效支撑动车组现场处置工作,在系统或模型报出诊断结果后,模型研发或运用单位等通常需要投入大量人力和精力对预警结果进行人工核验才能更为准确、有效地指导现场工作,效率较低,影响生产组织。为此,研究提出采用K最近邻和卷积神经网络等两种监督机器学习算法对PHM预警结果进行自动核验,并选取某型动车组牵引系统温度类PHM预警模型对算法进行了测试和验证。结果表明,该算法检验的准确率等指标较为理想,可以在一定程度上代替人工核验,提高模型结果的可用性。

基于PHM技术的EOAS健康管理和故障预测系统研究

目前铁路电务部门主要依靠人工和机务故障报修系统的提报信息,对动车组司机操控信息分析系统(EOAS)车载设备进行维护,但无效及误报信息较多,无法满足当前车载设备的运用保障需求。针对该问题,基于故障预测与健康管理(PHM)技术,利用EOAS实时回传的车载设备运行数据和图像信息,通过通用故障建模、大数据技术、图像识别,智能分析运行状态变化,实现EOAS车载设备健康状态在线实时监测、关键部件的故障报警及预测、EOAS全生命周期的健康信息管理,为设备日常运用、维护和管理提供技术手段,实现精准和预防性维修,把当前被动的故障处理转变为故障预防管理。该系统投入运用后,对相关设备维护人员的知识储备、经验能力、熟练程度等要求大大降低;与原人工检测作业对比,检测耗时大幅缩短,有效提升电务部门的设备维护效率和质量,保障了车载设备的持续可靠运行。

高铁牵引供电系统PHM与主动维护研究

随着我国高速铁路进入全面运营维护期,针对牵引供电系统与设备状态只能进行二元判断、故障诊断采用故障后处理方式以及维修维护只能采用被动模式等种种不足,提出将故障预测与健康管理(PHM)和主动维护应用于高速铁路牵引供电系统中,结合高速铁路牵引供电系统的多时空尺度性、动态性与随机性的特点,运用多尺度变换、时空联合分析以及随机过程等方法,实现故障的预测预警、系统健康状态的综合评估、全寿命周期可靠性的分析和风险的评估以及维修策略的决策与优化.研究结果表明:PHM与主动维护理论与方法的引入,能有效处理牵引供电系统的大量数据信息、综合表现从设备级到系统级的健康状态、在故障发生前及时预测预警并制定最优的主动维修维护策略;PHM与主动维护技术途径的实现,能在牵引供电系统处于不健康运行状态时及时做出应激反应,通过健康监控与智能维护系统一系列的感知、评估、预测、诊断与决策过程恢复牵引供电系统健康运行.

自动驾驶仪PHM系统健康评估方法研究

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。

故障预测与健康管理(PHM)技术的现状与发展

结合故障预测与健康管理(PHM)的技术发展过程,阐述了PHM的应用价值。论述了PHM技术系统级应用问题,提出了故障诊断与预测的人-机-环完整性认知模型,并依此对蓬勃发展的故障诊断与故障预测技术进行了分类与综合分析,给出了PHM技术的发展图像。针对故障诊断与预测的不确定性特征,对故障诊断与预测技术的性能要求、定量评价与验证方法进行了分析。最后,以PHM技术的工程应用为线索,提出了PHM技术发展中的几个问题。

PHM模型的工程化验证方法研究

模型验证技术在故障预测与健康管理(PHM)系统研制中受到高度重视,特别是如何将验证方法在具体的工程应用中体现规范性、系统性、通用性和实用性,已成为亟待解决的技术问题;在分析国内外相关研究现状的基础上,对PHM模型验证方法进行了系统深入的研究,阐述了故障数据获取方法和故障诊断和预测性能指标体系,并以机载诊断模型为例,对PHM模型验证流程作了详细介绍;最后,将此方法应用到了PHM验证平台的设计、开发和具体实现中,充分体现了PHM模型验证方法的工程化特点。

振动载荷下面向电子设备PHM的板级封装潜在故障分析方法

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.

基于PHM的航空装备可用度影响因素分析方法

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.

综合化航空电子系统PHM应用与设计

故障预测与健康管理(PHM)技术应用是保障电子系统任务完成成功率的重要手段和方法。对航空电子系统的PHM应用及设计思路进行了探讨,提出了PHM系统功能关系及适合综合化航空电子系统的健康管理体系。对PHM工程实现的主要关键技术及解决方案进行了研究,重点分析了基于测试性模型的多信号增强诊断方法,以及基于马尔科夫和贝叶斯网络的健康评估方法,对最终实现航空电子系统的PHM全面应用具有指导意义。

基于PHM的雷达装备维修保障研究

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。

机载导弹发射装置PHM系统总体设计

预测与健康管理(PHM)是近年来在军用装备研制中提出的一种新的保障技术手段。机载导弹发射装置作为机载武器系统的配套产品,已纳入飞机PHM系统设计中。本文介绍了PHM的基本概念和关键技术,并根据发射装置的功能特点,提出了在线和离线相结合的发射装置PHM框架结构,最后探讨了实施PHM部位原则、监测参数的选取以及故障预测和维护管理方法。

一种基于BP神经网络的集成电路PHM模型

提出了一种基于数据驱动的集成电路故障预测与健康管理(PHM)模型,该模型基于反向传播(BP)神经网络算法,避免了对集成电路老化失效物理机理的依赖,能有效拟合集成电路失效的非线性函数关系。以已编程应用设计的FPGA为目标器件,通过实验提取参数样本进行模型训练,并将模型应用于实测验证。结果表明,该模型输出结果与实测结果吻合良好,能有效满足集成电路故障预测与健康管理的实际应用。

数据挖掘技术在航空发动机PHM中的应用

总结了数据挖掘工具的方法和模式以及数据挖掘的流程,对其在航空发动机故障预测和健康管理(PHM)中的常用方法和模式,如神经网络、时间序列分析、模糊逻辑、遗传算法、证据理论、支持向量机等及其应用案例进行了阐述。