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.

An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

The prognostics health management(PHM)fromthe systematic viewis critical to the healthy continuous operation of processmanufacturing systems(PMS),with different kinds of dynamic interference events.This paper proposes a three leveled digital twinmodel for the systematic PHMof PMSs.The unit-leveled digital twinmodel of each basic device unit of PMSs is constructed based on edge computing,which can provide real-time monitoring and analysis of the device status.The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters,which are deployed for the manufacturing execution on the fog server.The shop-leveled digital twin maintenancemodel is designed for production planning,which gives production instructions fromthe private industrial cloud server.To cope with the dynamic disturbances of a PMS,a big data-driven framework is proposed to control the three-level digital twin models,which contains indicator prediction,influence evaluation,and decisionmaking.Finally,a case study with a real chemical fiber system is introduced to illustrate the effectiveness of the digital twin model with edge-fog-cloud computing for the systematic PHM of PMSs.The result demonstrates that the three-leveled digital twin model for the systematic PHM in PMSs works well in the system’s respects.

Battery prognostics and health management for electric vehicles under industry 4.0

Transportation electrification is essential for decarbonizing transport. Currently, lithium-ion batteries are the primary power source for electric vehicles (EVs). However, there is still a significant journey ahead before EVs can establish themselves as the dominant force in the global automotive market. Concerns such as range anxiety, battery aging, and safety issues remain significant challenges.

Virtual sample generation for model-based prognostics and health management of on-board high-speed train control system

In view of class imbalance in data-driven modeling for Prognostics and Health Management(PHM),existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train control equipment.A virtual sample generation solution based on Generative Adversarial Network(GAN)is proposed to overcome this shortcoming.Aiming at augmenting the sample classes with the imbalanced data problem,the GAN-based virtual sample generation strategy is embedded into the establishment of fault prediction models.Under the PHM framework of the on-board train control system,the virtual sample generation principle and the detailed procedures are presented.With the enhanced class-balancing mechanism and the designed sample augmentation logic,the PHM scheme of the on-board train control equipment has powerful data condition adaptability and can effectively predict the fault probability and life cycle status.Practical data from a specific type of on-board train control system is employed for the validation of the presented solution.The comparative results indicate that GAN-based sample augmentation is capable of achieving a desirable sample balancing level and enhancing the performance of correspondingly derived fault prediction models for the Condition-based Maintenance(CBM)operations.

Computational Reproducibility Within Prognostics and Health Management

Scientific research frequently involves the use of computational tools and methods.Providing thorough documentation,open-source code,and data–the creation of reproducible computational research(RCR)–helps others understand a researcher’s work.In this study,we investigate the state of reproducible computational research,broadly,and from within the field of prognostics and health management(PHM).In a text mining survey of more than 300 articles,we show that fewer than 1%of PHM researchers make their code and data available to others.To promote the RCR further,our work also highlights several personal benefits for those engaged in the practice.Finally,we introduce an open-source software tool,called PyPHM,to assist PHM researchers in accessing and preprocessing common industrial datasets.

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 Possibilistic Approach for Uncertainty Representation and Propagation in Similarity-Based Prognostic Health Management Solutions

In this paper, a data-driven prognostic model capable to deal with different sources of uncertainty is proposed. The main novelty factor is the application of a mathematical framework, namely a Random Fuzzy Variable (RFV) approach, for the representation and propagation of the different uncertainty sources affecting Prognostic Health Management (PHM) applications: measurement, future and model uncertainty. In this way, it is possible to deal not only with measurement noise and model parameters uncertainty due to the stochastic nature of the degradation process, but also with systematic effects, such as systematic errors in the measurement process, incomplete knowledge of the degradation process, subjective belief about model parameters. Furthermore, the low analytical complexity of the employed prognostic model allows to easily propagate the measurement and parameters uncertainty into the RUL forecast, with no need of extensive Monte Carlo loops, so that low requirements in terms of computation power are needed. The model has been applied to two real application cases, showing high accuracy output, resulting in a potentially effective tool for predictive maintenance in different industrial sectors.

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.

基于PHM技术的动车组牵引设备健康管理系统分析

阐述基于PHM技术的动车组牵引设备健康管理系统的设计和应用,通过对系统的需求、功能架构设计以及关键技术的分析,为动车组牵引设备的健康管理提供一个全面、实用的框架。

Review on Lithium-ion Battery PHM from the Perspective of Key PHM Steps

Prognostics and health management(PHM)has gotten considerable attention in the background of Industry 4.0.Battery PHM contributes to the reliable and safe operation of electric devices.Nevertheless,relevant reviews are still continuously updated over time.In this paper,we browsed extensive literature related to battery PHM from 2018to 2023 and summarized advances in battery PHM field,including battery testing and public datasets,fault diagnosis and prediction methods,health status estimation and health management methods.The last topic includes state of health estimation methods,remaining useful life prediction methods and predictive maintenance methods.Each of these categories is introduced and discussed in details.Based on this survey,we accordingly discuss challenges left to battery PHM,and provide future research opportunities.This research systematically reviews recent research about battery PHM from the perspective of key PHM steps and provide some valuable prospects for researchers and practitioners.

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。

动车组制动系统PHM人机界面设计方法研究

生态界面设计是一套复杂安全苛求系统人机界面的设计理论体系,它基于认知学和心理学的相关原理,能够帮助操作人员在人机交互中面临新情况和问题时做出正确决策,从而提高操作绩效。文中以复兴号动车组制动系统故障预测与健康管理信息界面为研究对象,依据生态界面设计理论,采用抽象层次法进行工作领域分析,确定了制动系统的功能性目的、抽象功能、一般性功能、物理性功能和物理形式的具体内容,提出了界面的模块划分和具体视图方案。通过软件实现了仿真界面的显示并开展了故障处理对比试验。研究工作表明使用生态界面设计方法对动车组制动系统故障预测与健康管理信息界面进行设计,能够提高故障处理绩效和操作人员对系统的理解水平,可为其他车载人机接口界面设计提供参考。

一种形式化目标控制器PHM设计方法

为了实现目标控制器的故障预测,指导预防性维修,降低检修成本,降低运营故障率,提高产品竞争力,需对目标控制器进行PHM设计。本文提出PHM设计的首要问题是确定设备的关键性能参数及裕量,并引入一种基于确信可靠性理论的形式化分析方法,确定目标控制器的关键性能参数与性能裕量,为后续的PHM设计提供依据。

PHM系统设计方案费效评估模型

故障预测与健康管理(PHM)系统是新一代装备安全可靠性的使能技术,已逐步应用于复杂装备密集的行业。针对其设计方案的评估问题,分析了应用PHM系统投入的成本和维修费用,建立了PHM效益模型和装备故障率估算模型,并提出PHM费用效果评估方法,通过案例分析,说明模型的应用程序和效果。

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。

轨道交通装备PHM技术现状与发展趋势

随着我国轨道交通装备的运行里程和运营速度不断提升,故障预测与健康管理(PHM)技术是保障其持续长距离、大规模、高密度运营的安全可靠性的关键技术之一。文章总结了近年来轨道交通装备PHM技术的国内外应用情况和技术特点,梳理了状态监测、故障诊断、故障预测、寿命预测、智能运维等关键技术的研究方法与发展趋势。结合人工智能(AI)、机器学习、云计算等信息技术,未来轨道交通装备PHM技术将趋向AI驱动的智能化、自动化综合性健康管理和智能运维,提高可用性、安全性和效率,降低全寿命维护成本,推动轨道交通的可持续发展。

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。

机载预测与健康管理(PHM)系统的体系结构

故障预测与健康管理(Prognostics and Health Management PHM)系统对于推动作战飞机从"事后维修"、"定时维修"向"视情维修"转变具有十分重要的意义。针对新一代作战飞机的技术特点以及在维修保障方面的需求,对机载PHM系统体系结构的3种备选方案进行了对比分析,提出了一种由模块/单元层PHM、子系统级PHM、区域级PHM和平台级PHM等4层集成的层次化体系结构,并着重从层次的划分、组成要素的功能描述、信息传输和外部逻辑等几个方面进行了论述。