Design of a Prognostics and Health Management System for Electromechanical Equipment Through Time Stress Analysis

Time stress includes all kinds of environment and operating stress such as shock, vibration, temperature and electric current that the electromechanical system suffers in the manufacture, transport and operating process. In this paper, the conception of time stress and prognostics and health management （ PHM） system are introduced. Then, in order to improve the false alarm recognition and fault prediction capabilities of the electromechanical equipment, a novel PHM architecture for electromechanical equipment is put forward based on a built-in test （BIT） system design technology and time stress analysis method. Finally, the structure, the design and implementing method and the functions of each module of this PHM system are described in detail.

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 novel testability model for health management of heading attitude system

Prognostics and health management （PHM） is very important to guarantee the reliability and safety of aerospace systems, and sensing and test are the precondition of PHM. Integrating design for testability into early design stage of system early design stage is deemed as a fundamental way to improve PHM performance, and testability model is the base of testability analysis and design. This paper discusses a hierarchical model-based approach to testability modeling and analysis for heading attitude system health management. Quantified directed graph, of which the nodes represent components and tests and the directed edges represent fault propagation paths, is used to describe fault-test dependency, and quantitative testability information is assigned to nodes and directed edges. The fault dependencies between nodes can be obtained by functional fault analysis methodology that captures the physical architecture and material flows such as energy, heat, data, and so on. By incorporating physics of failure models into component, the dynamic process of a failing or degrading component can be projected onto system behavior, i.e., system symptoms. Then, the analysis of extended failure modes, mechanisms and effects is utilized to construct fault evolution-test dependency. Using this integrated model, the designers and system analysts can assess the test suite＇s fault detectability, fault isolability and fault predictability. And heading attitude system application results show that the proposed model can support testability analysis and design for PHM very well.

Rolling bearing fault diagnostics based on improved data augmentation and ConvNet

Convolutional neural networks(CNNs)are well suited to bearing fault classification due to their ability to learn discriminative spectro-temporal patterns.However,gathering sufficient cases of faulty conditions in real-world engineering scenarios to train an intelligent diagnosis system is challenging.This paper proposes a fault diagnosis method combining several augmentation schemes to alleviate the problem of limited fault data.We begin by identifying relevant parameters that influence the construction of a spectrogram.We leverage the uncertainty principle in processing time-frequency domain signals,making it impossible to simultaneously achieve good time and frequency resolutions.A key determinant of this phenomenon is the window function's choice and length used in implementing the shorttime Fourier transform.The Gaussian,Kaiser,and rectangular windows are selected in the experimentation due to their diverse characteristics.The overlap parameter's size also influences the outcome and resolution of the spectrogram.A 50%overlap is used in the original data transformation,and±25%is used in implementing an effective augmentation policy to which two-stage regular CNN can be applied to achieve improved performance.The best model reaches an accuracy of 99.98%and a cross-domain accuracy of 92.54%.When combined with data augmentation,the proposed model yields cutting-edge results.

基于BP神经网络和遗传算法的设备故障诊断与健康管理模型研究

针对目前设备管理存在的故障处理周期长、维护保养任务重、维修成本高的现状,构建了设备故障诊断与健康管理架构,包括设备层、感知层、数据处理及存储层、数据分析层和应用层。其中,在数据分析层,综合采用BP神经网络和遗传算法,建立了设备故障诊断与健康管理模型。最后,以机电设备振动数据为例,进行设备故障诊断模型的预测结果分析,验证了该模型的可行性。研究结果表明,该模型能提高设备故障诊断正确率,具有较好的故障诊断效果;设备预测健康状态与实际健康状态的变化趋势基本保持一致,重合率大于90%。该成果可为制造企业的设备故障诊断与健康管理提供相关策略,有效排除故障问题,降低管理成本。

火炮供输弹系统故障诊断方法研究进展与趋势

由机械、电气、液压高度耦合而成的火炮供输弹系统是火炮实现自动化的核心关键系统。复杂性强、任务作业环境恶劣,导致供输弹系统故障率高、可靠性差,从而影响火炮系统的整体性能,对其开展故障研究尤为重要。以火炮供输弹系统为研究对象,分析了供输弹系统故障所呈现的特点,总结了典型的供输弹系统故障分析方法,概述了供输弹系统故障诊断方法的研究进展和存在的问题。此外,当前故障诊断技术的发展和应用也为提高火炮供输弹系统的可靠性提供了有效的途径,通过了解多领域故障诊断方法的研究历程,探讨了供输弹系统故障诊断技术未来的发展趋势,并归纳了影响供输弹系统故障诊断研究的个性因素,为其之后的探索提供了研究思路。

Deep neural network based classification of rolling element bearings and health degradation through comprehensive vibration signal analysis

Rolling element bearings are machine components used to allow circular movement and hence deliver forces between components of machines used in diverse areas of industry.The likelihood of failure has the propensity of increasing under prolonged operation and varying working conditions.Hence, the accurate fault severity categorization of bearings is vital in diagnosing faults that arise in rotating machinery.The variability and complexity of the recorded vibration signals pose a great hurdle to distinguishing unique characteristic fault features.In this paper, the efficacy and the leverage of a pre-trained convolutional neural network(CNN) is harnessed in the implementation of a robust fault classification model.In the absence of sufficient data, this method has a high-performance rate.Initially, a modified VGG16 architecture is used to extract discriminating features from new samples and serves as input to a classifier.The raw vibration data are strategically segmented and transformed into two representations which are trained separately and jointly.The proposed approach is carried out on bearing vibration data and shows high-performance results.In addition to successfully implementing a robust fault classification model, a prognostic framework is developed by constructing a health indicator(HI) under varying operating conditions for a given fault condition.

机车走行部旋转部件健康评估系统

为推进机车走行部旋转部件实施状态修,在利用机车走行部在线监测系统采集的相关数据以及机车车辆走行部专家系统积累的故障诊断知识的基础上,建立机车走行部旋转部件健康评估系统,采用多维数据融合分析方法,完成机车走行部旋转部件健康状态评估,给出旋转部件健康评估结论和维修建议。该系统已在多个机务段投入应用,其适用性得到初步验证,能够提前发现旋转部件故障,输出合理的维修建议,有利于优化机车维修策略,为实现机车状态修奠定技术基础。

面向售后保障的导弹装备健康管理系统研究

随着战争向信息化、体系化、智能化方向发展,部队用户对导弹装备保障的需求从事后维修、定期维护向视情维修和智能保障转变,对导弹装备故障预测与健康管理提出了更高要求。通过开展导弹装备健康管理系统的需求分析,提出了面向导弹装备保障的系统总体架构、面向军厂复杂应用场景的系统物理架构和基于微服务的系统软件技术架构,采用结构化设计方法开展了系统功能模块结构设计,对系统关键技术进行了阐述和分析,并完成了面向试点型号的系统阶段性建设和应用。应用结果表明了本研究的可行性,可为其他型号装备的健康管理提供参考。

综采设备健康状态评估研究综述

综采设备逐渐趋于大型化、复杂化、智能化,定期维修与事后维修等传统设备管理模式已难以满足煤矿智能化建设对设备运行的高可靠性需求。因此,研究综采设备健康状态评估相关理论及技术对煤矿智能开采技术发展意义重大。给出了综采设备健康状态评估范畴界定及综采设备健康状态评估流程。从综采设备信号获取、特征提取及融合、健康状态等级划分、健康状态评估模型建立4个方面总结了综采设备健康状态评估方法的研究现状和发展动态。分别从综采设备信号获取及传感器优化布置、数据处理及特征提取、健康状态评估模型建立、综采设备状态评估应用等方面分析了综采设备健康状态评估相关技术目前面临的挑战。针对上述研究现状及面临的挑战,从数据采集方案及故障机理研究手段提升、大数据高性能计算平台建设、深度学习评估模型建立、综采设备健康状态动态评估模型研究、综采设备健康状态评估系统开发等方面探讨了综采设备健康状态评估技术的发展趋势,指出在煤矿智能化进程中,需确保综采设备健康状态评估理论研究、算法开发和工程应用三线齐头并进。

煤矿坑道钻机状态监测与故障诊断技术研究现状及展望

煤矿坑道钻机自动化和智能化程度的提高、作业范围和应用场景的扩展,使其系统复杂性和工作环境恶劣程度随之增加,对煤矿坑道钻机可靠性提出新的挑战。为有效降低因钻机故障带来的安全风险,提升钻机全寿命周期的任务品质,煤矿坑道钻机状态监测与故障诊断技术应运而生。首先从钻机状态参数采集、数据处理、诊断方法、系统应用等四个方面对煤矿坑道钻机关键系统状态监测和故障诊断技术研究现状进行总结分析,然后在故障预测与健康管理(prognostics and health management, PHM)技术基本概念的基础上根据集中式与分布式结合的方式提出了坑道钻机PHM技术框架,最后对煤矿坑道钻机PHM技术发展趋势进行展望,旨在为新一代煤矿坑道钻机及相关煤矿机械装备PHM技术的研究开展提供参考和借鉴。

智能化雷达故障预测及检测技术综述

雷达故障预测和故障检测技术是雷达装备维护从传统化定期检修向智能化视情维修转变的关键技术。为保障雷达作战效能的发挥,需及时对雷达故障进行预测、检测并实时告警。随着微波测量和人工智能技术的日趋成熟,智能化雷达故障预测及检测技术也不断发展。文中详细阐述了当前故障预测与健康管理以及故障检测技术的国内外研究现状,分析了现有智能化雷达故障预测和检测技术的优缺点,梳理了该技术在雷达维修保障领域的研究进展,提出了雷达故障预测和检测过程中可能存在的问题和限制条件。针对实际问题和限制条件,对未来智能化雷达故障预测和检测技术的研究方向进行了展望,为智能化故障预测及故障检测技术在雷达维修保障领域的深入研究提供参考。

城市轨道交通设备智能运维系统设计及关键技术研究

针对现有城市轨道交通(简称:城轨)设备状态信息采集不完整、设备运维管理缺少决策支持数据、各专业运维业务缺乏统筹协调、运维效率低等现实问题,着眼于城轨设备综合运维管理,基于故障预测与健康管理(PHM,Prognostics Health Management)理念,提出城轨设备智能运维系统的总体架构和功能框架,探讨亟需深入研究的关键技术。该系统通过采集和利用大量设备监测数据,利用故障诊断模型、人工智能算法和工作流引擎,在实现关键设备健康管理的基础上,自动生成设备运维计划,辅助设备运维管理决策,支持多业务作业协同,有助于提高城轨设备整体运维效能,提高城轨设备健康水平和性能,最小化停运时间,降低维修保障费用,保障城轨安全、可靠、高效运营。

船舶设备故障预测和健康管理产业发展现状及趋势研究

在对船舶设备故障预测和健康管理流程及组成进行简要概述的基础上,重点分析其技术发展现状、市场竞争格局,并从行业政策、前沿技术整合及市场需求等3个方面总结船舶设备故障预测和健康管理产业发展趋势,从而提出我国船舶设备故障预测和健康管理产业发展建议。

中国高速列车健康监测与管理:进展及展望

随着列车运营速度不断提升、配属规模及车辆种类不断扩展,加之受长交路、多物理场耦合等复杂服役环境的影响,高速列车安全保障及经济运维的要求持续提高;高速列车健康监测与管理技术的研究与应用,为中国高速铁路的长距离、大规模、高密度运营提供了关键支撑。本文阐述了健康监测与管理对高速列车的重要价值,回顾了近20年中国高速列车健康监测与管理的发展历程:从安全监控到关键系统健康监测,再到一体化、全寿命周期的运维管理;总结了列车全方位状态监测、精准评估与诊断预测、车辆远程运维服务、智能运维决策支持等方面的重大技术突破。进一步展望了广域全过程适应性、列车数据/计算资源一体化管理与应用、基于健康监测与管理的列车设计、车–线–站一体化智能运维等未来发展方向,以期应对中国高速铁路面临的高效安全运维、深度降本降耗等发展挑战,推动中国高速列车技术持续领先。

智能变电站故障预测与健康管理关键技术及展望

随着智能变电站系统自动化程度的快速发展以及新型传感器的广泛应用,智能变电站内电气设备数据挖掘直接关系着智能变电站的可靠性、稳定性和安全性。针对大数据背景下智能变电站的智能运维,着重介绍故障预测与健康管理(prognostics and health management,PHM)技术在智能变电站的应用,并采用状态检修系统开放体系架构(open system architecture condition-based management,OSA-CBM)标准对PHM系统的层次结构进行了梳理。首先,在对智能变电站系统结构进行分析的基础上,重点总结了可用于智能变电站的基于数据驱动的各种故障诊断及寿命预测方法;然后,系统分析了智能变电站中的检修管理的研究成果;最后,基于OSA-CBM架构提出了一种可用于智能变电站的PHM架构。

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。

PLC与5G融合通信在铁路信号设备状态监测系统中的应用研究

针对铁路信号设备状态监测系统中常见的PLC+ADSL组网模式存在硬件设备复杂、带宽有限、维护难度大等问题,分析了铁路5G专网建设情况及应用场景,研究了PLC+5G专网融合通信在道岔缺口监测系统、信号设备故障预测与健康管理系统中的组网及部署方案,实现了监测数据的实时上报,并在包神铁路现场进行了验证。结果表明:相较于PLC+ADSL组网模式,PLC+5G的融合通信实时性更高、可扩展性更强、部署简单更灵活,具有较高的工程应用价值。

基于对比学习的滚动轴承早期故障在线检测方法

早期故障检测不仅是故障诊断与设备健康管理的难点,也是工业生产实践中亟待攻坚的技术重点。该研究在工业大数据驱动视域下,提出了一种基于对比学习的滚动轴承异常及早期故障在线检测方法。首先,建立一个基于深度可分离卷积及残差连接的深度编码器,在能有效提取信号特征的同时进一步降低模型参数量和计算量;其次,设置特定代理任务以实现基于对比学习方法的无监督编码器训练,使编码器胜任不同采样点信号之间差异的学习任务;最后,通过训练后的编码器对信号进行特征提取,并设计一种在线检测算法,该算法能够识别并区分滚动轴承的异常及早期故障。该研究引入XJTU-SY数据集对上述方法进行验证,结果表明,与现有无监督故障检测方法相比,该方法准确性高,时效性强,丰富了不同工况下轴承全生命周期的健康管理方法。

基于ELM-AE的轴承性能退化和早期故障检测

为准确描述轴承性能退化过程并实现轴承早期故障检测,提出了一种基于极限学习机自编码器的无监督构建健康指标的方法。利用轴承健康状态的样本训练极限学习机自编码器并实现在线样本数据的重构,由于轴承偏离健康状态时会导致输入与输出的重构误差增大,因此将重构数据与原始数据之间的损失作为反映轴承性能退化的健康指标。试验结果表明,该指标能够有效反映轴承性能退化趋势,对轴承早期故障比较敏感,利用滑动t检验可以准确获得健康指标首次发生突变的时间,可用于轴承的智能化早期故障预警。