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.

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.

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.

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 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.

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。

Prognostics and health management of alkaline water electrolyzer: Techno-economic analysis considering replacement moment

Recently,considerable attention has been paid to the installation of renewable energy capacity to mitigate global CO_(2) emissions.H_(2) produced using water electrolysis and renewable energy is regarded as a clean energy carrier,generating electricity without CO_(2) emissions,called‘Green H 2’.In this paper,a prognostics and health man-agement model for an alkaline water electrolyzer was proposed to predict the load voltage on the electrolyzer to obtain the state of health information.The prognostics and health management model was developed by training historical operating data via machine learning models,support vector machine and gaussian process regression,showing the root mean square error of 1.28×10^(−3) and 8.03×10^(−6).In addition,a techno-economic analysis was performed for a green H_(2) production system,composed of 1 MW of photovoltaic plant and 1 MW of alkaline water electrolyzer,to provide economic insights and feasibility of the system.A levelized cost of H_(2) of$6.89 kgH_(2)−1 was calculated and the potential to reach the levelized cost of H_(2) from steam methane reforming with carbon capture and storage was shown by considering the learning rate of the photovoltaic module and elec-trolyzer.Finally,the replacement of the alkaline water electrolyzer at around 10 years was preferred to increase the net present value from the green H_(2) production system when capital expenditure and replacement cost are low enough.

地面预警监视雷达PHM技术现状与应用发展研究

为了更好地促进故障预测与健康管理(PHM)技术在地面预警监视雷达中的发展与应用,提升部队自主保障能力和效率,以及降低部队保障压力,文中首先从系统架构、数据采集、数据处理、状态监测、故障预测、健康评估、维修决策等方面阐述了与地面预警监视雷达相关的PHM技术的研究现状;然后指出了地面预警监视雷达PHM技术在走向有效的工程化应用方面所面临的状态参数和特征信号难以获取,故障预测技术还不成熟,雷达PHM研制和验收无标准可依等制约因素;最后根据地面预警监视雷达的技术发展趋势以及使用和维修保障特点,从面向雷达PHM需求的测试性设计、大数据云平台PHM体系架构、基于数据驱动的预测技术和雷达PHM标准研究等方面展望了雷达PHM技术需进一步研究的内容和方向。

列车运行控制系统车载设备PHM实施方案

列车运行控制系统车载设备(简称:列控车载设备)是一种高度集成化的电子设备,针对其维护难点,提出将故障预测及健康管理(PHM,Prognostics and Health Management)技术引入列控车载设备维护。文章基于设备全生命周期管理理念,提出列控车载设备PHM实施方案,将设备功能需求与维修需求融合一体,使列控车载设备PHM系统的研发与列控车载设备的升级改造相协调,通过列控车载设备加装升级、数据处理与分析系统建设,在完善列控车载设备BIT和数据采集与分析功能的基础上,构建列控车载设备健康评估系统。并制定了列控车载设备PHM实施计划,稳步推进相关设备研制及系统研发与建设工作,使维修保障部门能够在列控车载设备健康评估系统支持下高效协同工作,实现故障处置闭环管理,推动列控车载设备维修转向视情维修模式。

Boosting battery state of health estimation based on self-supervised learning

State of health(SoH) estimation plays a key role in smart battery health prognostic and management.However,poor generalization,lack of labeled data,and unused measurements during aging are still major challenges to accurate SoH estimation.Toward this end,this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation.Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells,the proposed method achieves accurate and robust estimations using limited labeled data.A filter-based data preprocessing technique,which enables the extraction of partial capacity-voltage curves under dynamic charging profiles,is applied at first.Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder.The learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data,which boosts the performance of the estimation framework.The proposed method has been validated under different battery chemistries,formats,operating conditions,and ambient.The estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles,with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%,and robustness increases with aging.Comparisons with other pure supervised machine learning methods demonstrate the superiority of the proposed method.This simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.

基于PHM的发动机轴承状态维修决策研究

故障预测与健康管理(PHM)是美国针对先进装备提出的一种维修保障技术,可实现对装备的状态监控、故障诊断与预测、寿命预测、健康管理和状态维修决策。PHM体系结构中的两个重要环节是剩余寿命预测和状态维修决策。利用灰色GM(1,1)模型,编程实现了某型发动机轴承剩余寿命预测系统,描述了系统的主要功能模块。然后,应用威布尔比例风险模型实现了某型发动机轴承的状态维修决策,验证了该模型的实用性和合理性。

公共交通PHM的研究与应用

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。

面向PHM的雷达接收机传感器配置方法

故障预测与健康管理(PHM)系统在装备健康状态的评估以及视情维修方面起着重要的作用。作为收集装备系统健康信息的传感器,在PHM系统中地位也极其重要。基于雷达接收机系统特点,对传感器的种类和特性进行了分析,建立了配置模型,利用条件函数对目标函数进行了优化,得到了传感器的配置方案,该方法将对各型雷达接收机的传感器配置提供有益参考。

知识图谱技术在预测与健康管理中的应用现状与研究展望

随着预测与健康管理(PHM)技术的不断发展以及设备智能化、信息化程度的不断提高,预测与健康管理的领域知识与日俱增。知识图谱技术因其强大的知识组织、管理、表示能力以及支持的数据/知识驱动相关方法,受到领域内学者广泛关注。面向预测与健康管理领域,对知识图谱的概念、关键技术、领域应用以及挑战与展望进行了综述。首先,介绍了领域知识图谱的定义和组成要素;其次,讨论了领域知识图谱的构建方法,简要归纳了领域内常用的构建方式和技术;然后,结合领域内知识特点,详细介绍了领域内知识图谱的应用情况;最后,分析了与领域融合的知识图谱研究中存在的挑战以及未来的发展方向。综述旨在帮助研究者加深对知识图谱及其在预测与健康管理领域应用的了解,以促进知识图谱在该领域应用中的进一步发展和创新。

轨道交通列车新一代健康管理系统架构研究

科学维护运营车辆、保障列车运行安全一直以来都是轨道交通领域的核心问题。近年来,随着列车预测性维修和无人驾驶等重大需求的提出,迫切需要实现全息状态感知、精细诊断预测、及时反馈处置的列车健康管理功能。现有系统架构存在着感知低效、融合深度不足、模型优化动态差、计算协同弱、自主化决策水平低等问题,先进的物联网、大数据、人工智能、数字孪生等技术的发展,推动了列车健康管理系统架构向更高水平智能化演进。文章对列车健康管理系统技术发展阶段进行了梳理划分,在此基础上提出了基于泛在感知与协同计算的轨道交通列车健康管理系统4.0架构,详细阐述了其内涵概念、系统架构和关键技术,归纳出泛在感知、协同计算与健康管理深度融合的解决途径、技术手段和预期效果,明确了现阶段技术攻关的主要方向,进而支撑列车安全保障和运维品质的提升。