需求随机波动下多设备批量生产系统的视情维护策略研究

针对需求随机波动情况下多设备批量生产系统的设备维护问题,提出了一种基于滚动生产计划和设备退化状况的视情维护策略。首先,通过滚动时域规划方法预测不同产品的随机需求并在此基础上以总生产成本最小确定滚动生产计划。其次,在每一滚动生产周期开始前检测系统中各设备的退化水平,利用Gamma过程描述退化增量,以最小维护成本率确定当前退化状态下各设备的最佳维护时间,同时为避免生产过程中断利用提前延后维护策略对预防维护进行动态调整。在系统层,利用生产转换时机对需要维护的组件进行组合维护。再次,引入时间约束和服务水平约束,建立批量生产与视情维护的联合优化模型,以总成本最小为目标,确定实际生产计划和维护计划。最后,通过算例以整个生产计划期内的总成本和故障次数为度量验证了所提出的多设备批量生产系统视情维护策略的有效性。     

基于机器学习的设备预测性维护方法综述

机器学习算法能够处理高维和多变量数据,并在复杂和动态环境中提取数据中的隐藏关系,在预测性维护技术中具有很好的应用前景。然而,预测性维护系统的性能取决于机器学习算法的选择,对目前应用与预测性维护中的机器学习算法进行综述,详细比较了几种机器学习算法的优缺点,并对机器学习在预测性维护研究中的应用进行了展望。     

机载中央维护系统通信建模及实现

针对国内民航领域内难以从机载中央维护系统(CMS)自主获取航电系统维护数据的问题,研究提取维护数据的技术并予以实现.通过CMS系统的解析和研究,建立中央维护系统各交联计算机之间的通信模型,实现维护测试的工作模式,设计了中央维护系统接口仿真器,实现对真实航电组件维护数据的提取和显示;最后,通过在实际航电系统环境下的联合测试,验证仿真器对维护总线数据的处理能力.测试结果表明,仿真器能够可靠地解码和提取现代民用飞机航电系统维护总线数据,满足航电系统集成仿真验证的需求.     

相依多模失效下冷贮备系统最优切换及视情维护决策

针对存在冲击影响的冷贮备系统,研究其最优切换及视情维护决策问题.首先,在系统结构和切换式运行和维护特性分析的基础上,制定基于周期切换和状态检测的切换式离线视情维护策略;其次,建立累积冲击过程影响下系统退化所致的软失效和极端冲击过程所致的硬失效竞争可靠性模型;再次,通过分析两类冲击过程影响下系统运行与备用设备交替使用、维修过程中的状态转移特性,重点推导各检测周期时刻系统状态概率分布的迭代计算模型;然后,以系统平均费用率最小为目标,建立解析决策模型,以求解系统的最优切换周期和维护阈值.最后,以矿井主通风系统为案例验证策略及模型的有效性,并分析模型对参数的灵敏度.结果表明,系统的最优维修策略随机冲击影响的不同而变化显著.     

广播电视系统状态维护技术初探

随着广播电视技术发展速度的不断加快,人们对广播电视的质量提出了较高的要求,而对广播电视系统的维修管理和电视质量的提高具有重要的保障作用.本文首先对广播电视系统状态维护技术的预测性维修和可靠性评估等内容进行了简要阐述,然后从系统构建的详细设计两个方面对广播电视系统状态维护技术进行了初次探究.     

工业人工智能的关键技术及其在预测性维护中的应用现状

随着人工智能技术的快速发展及其在工业系统中卓有成效的应用, 工业智能化成为当前工业生产转型的一个重要趋势. 论文提炼了工业人工智能(Industrial artificial intelligence, IAI)的建模、诊断、预测、优化、决策以及智能芯片等共性关键技术, 总结了生产过程监控与产品质量检测等4个主要应用场景. 同时, 论文选择预测性维护作为工业人工智能的典型应用场景, 以工业设备的闭环智能维护形式, 分别从模型方法、数据方法以及融合方法出发, 系统的总结和分析了设备的寿命预测技术和维护决策理论, 展示了人工智能技术在促进工业生产安全、降本、增效、提质等方面的重要作用. 最后, 探讨了工业人工智能研究所面临的问题以及未来的研究方向.     

公共教学网络的管理和维护

本文针对用于公共教学的Novell网络系统用户数量多，管理复杂的情点，从网络规划和设计、目录和用户的管理、系统参数的调整和卷损坏的修复等几个方面讨论了网络系统的管理和维护方法．     

基于退化机器模型的分布式柔性生产系统:性能分析、任务调度及预测性维护

在近些年的制造环境中,由于市场对多品种、小批量定制产品需求的增加,生产制造更加深入地向着柔性方向发展.如何利用现有资源,提高生产效率,实时地对系统性能进行评估与预测,并对基于小批量生产的实时调度进行优化改进,在分布式柔性生产系统中具有重要的研究意义.因此,基于退化机器模型的多批次串行生产线的性能进行分析,并对分布式生产系统进行任务调度及预测性维护.具体地说,对于具有退化机器模型及有限容量缓冲区的生产系统,首先采用马尔科夫分析方法建立数学模型;随后,提出精确方法来计算此生产系统模型实时的性能指标,并针对该模型下的调度问题,设计最优完成时间指标优化算法;此外,提出基于退化机器模型的预测性维护策略以减少完成时间;最后,通过数值实验验证该算法的可行性和有效性.     

冷/温混合贮备系统最优切换及视情维修决策

针对周期性切换冷/温混合贮备系统,研究其最优切换以及视情维修决策,在系统劣化建模的基础上,分析系统结构和切换式运行维修特性,制定基于周期切换和检测的离线视情维修策略.首先,通过分析系统运行与备用设备交替使用、维修过程中的状态转移特性,推导各检测周期时刻系统状态概率分布模型以及各维修活动的概率;然后,以系统有限时间范围内平均费用率最小为目标建立解析优化模型,以决策最优切换周期和维护阈值,并采用遗传算法对模型进行求解;最后,以汽轮发电机定子冷却水泵系统为对象验证策略和模型的正确性和有效性,并对参数进行灵敏度分析.实验结果表明,所提出离线视情维修策略能够有效地降低系统的维修成本.     

浅谈计算机信息系统维护管理技术

系统维护的目的是要保证计算机信息系统正常而可靠地运行,并能使系统不断得到改善和提高,以充分发挥作用,系统维护的任务就是要有计划、有组织地对系统进行必要的改动,以保证系统中的各个要素随着环境的变化始终处于最新的、正确的工作状态.系统维护包括检测、更改和提高系统性能.本文主要从维护流程、维护原则、维护的常用技术进行探讨计算机信息系统维护管理技术.     

武器装备基于状态的维修系统设计

为了减少武器装备的故障以及维修时间,提高武器装备的可用度和重要部件的使用寿命,采用基于状态的维修技术与方法已成为当前维修领域研究与应用的热点.从武器装备的维修需求与技术出发,分析了现行装备维修的主要方式及其优缺点,总结了当前基于状态的维修(CBM)研究与应用现状,在此基础上提出了武器装备基于案例的CBM系统框架,给出了CBM适用的条件,并对CBM系统的核心模块进行了分析,给出了CBM系统工作的流程.最后,结合某装备中一齿轮箱的状态检测信息,进行了基于声音的装备故障诊断与案例分析与决策.分析结果表明:基于案例的CBM系统简单实用,能够满足装备维修需求.     

基于状态的维修及其建模研究

基于状态的维修（Condition Based Maintenanec，CBM）是设备（武器系统）预报初始故障的主动维修的一种形式。是设备维修的重耍发展方向之一，也是本世纪初国内外维修领域研究的热点课题。为进一步推进CBM理论在国内的研究与应用，该文首先介绍了CBM的概念，分析了其在技术上的可行性，与定期（时）维修作比较，说明了CBM的优越性。其次，重点构造了单状态信息的CBM的模型，该模型实现了预防性维修的优化决策。最后，给出了该模型仿真软件的结构框图，该文对CBM理论的进一步研究具有十分重要的意义。     

An overview of time-based and condition-based maintenance in industrial application

This paper presents an overview of two maintenance techniques widely discussed in the literature: time-based maintenance (TBM) and condition-based maintenance (CBM). The paper discusses how the TBM and CBM techniques work toward maintenance decision making. Recent research articles covering the application of each technique are reviewed. The paper then compares the challenges of implementing each technique from a practical point of view, focusing on the issues of required data determination and collection, data analysis/modelling, and decision making. The paper concludes with significant considerations for future research. Each of the techniques was found to have unique concepts/principles, procedures, and challenges for real industrial practise. It can be concluded that the application of the CBM technique is more realistic, and thus more worthwhile to apply, than the TBM one. However, further research on CBM must be carried out in order to make it more realistic for making maintenance decisions. The paper provides useful information regarding the application of the TBM and CBM techniques in maintenance decision making and explores the challenges in implementing each technique from a practical perspective.     

Condition-based Maintenance and the product improvement process

The evolution of enterprise services is changing the approach for enabling Product Lifecycle Management (PLM) and Supply Chain Management (SCM) business processes. Enabling systems are migrating to process- and service-oriented solutions. In particular, the paper demonstrates how the new technologies can be used to enable a critical process that links vehicle health maintenance to PLM. Our hypothesis is that Condition-based Maintenance (CBM) and PLM integration is achievable through composite application design. The key process for linking CBM to PLM must convert prognostic and diagnostic information into actionable information that can be directed into a project-level PLM environment that supports the end-to-end product improvement process. To test this hypothesis, we designed a composite application within the context of a Small Business Innovative Research project that is sponsored by the US government. This paper motivates the problem from the strategic level to the implementation level and describes the successful test of the hypothesis.     

加速冲击损伤退化系统剩余寿命预测及预测维修决策

随着检测传感技术的发展,诸如风力发电机叶片等可对其状态进行检测,并依据检测结果进行剩余寿命预测.但此类系统在运行中受环境冲击影响较大,如何对冲击影响下的系统剩余寿命进行预测,并结合预测结果进行经济可靠的维修决策是一个值得研究的问题.对此,针对状态可检测的连续退化系统,研究考虑加速冲击损伤特性下的系统剩余寿命预测及基于预测的维修决策.首先,考虑自然退化和与退化相关的冲击损伤,构建加速冲击损伤退化模型和剩余寿命预测模型;其次,制定基于周期检测的状态维修与预测维修相结合的混合维修策略,并推导不同维修活动的发生概率;然后,构建以长期平均费用率最小为目标,以检测间隔和故障率阈值为决策变量的决策模型,并给出了优化解法;最后,以风力发电机叶片为案例验证模型的适用性和有效性,对系统的参数进行灵敏度分析,并与未考虑加速冲击损伤和未考虑预测的维修决策结果进行对比分析.     

Enabling predictive maintenance integrated production scheduling by operation-specific health prognostics with generative deep learning

Predictive Maintenance (PdM) is one of the core innovations in recent years that sparks interest in both research and industry. While researchers develop more and more complex machine learning (ML) models to predict the remaining useful life (RUL), most models are not designed with regard to actual industrial practice and are not validated with industrial data. To overcome this gap between research and industry and to create added value, we propose a holistic framework that aims at directly integrating PdM models with production scheduling. To enable PdM-integrated production scheduling (PdM-IPS), an operation-specific health prognostics model is required. Therefore, we propose a generative deep learning model based on the conditional variational autoencoder (CVAE) that can derive an operation-specific health indicator (HI) from large-scale industrial condition monitoring (CM) data. We choose this unsupervised learning approach to cope with one of the biggest challenges of applying PdM in industry: the lack of labelled failure data. The health prognostics model provides a quantitative measure of degradation given a specific production sequence and thus enables PdM-IPS. The framework is validated both on NASA’s C-MAPSS data set as well as real industrial data from machining centers for automotive component manufacturing. The results indicate that the approach can both capture and quantify changes in machine condition such that PdM-IPS can be subsequently realized.     

A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies

CBM (Condition Based Maintenance) solutions are increasingly present in industrial systems due to two main circumstances: rapid evolution, without precedents, in the capture and analysis of data and significant cost reduction of supporting technologies. CBM programs in industrial systems can become extremely complex, especially when considering the effective introduction of new capabilities provided by PHM (Prognostics and Health Management) and E-maintenance disciplines. In this scenario, any CBM solution involves the management of numerous technical aspects, that the maintenance manager needs to understand, in order to be implemented properly and effectively, according to the company’s strategy. This paper provides a comprehensive representation of the key components of a generic CBM solution, this is presented using a framework or supporting structure for an effective management of the CBM programs. The concept “symptom of failure”, its corresponding analysis techniques (introduced by ISO 13379-1 and linked with RCM/FMEA analysis), and other international standard for CBM open-software application development (for instance, ISO 13374 and OSA-CBM), are used in the paper for the development of the framework. An original template has been developed, adopting the formal structure of RCM analysis templates, to integrate the information of the PHM techniques used to capture the failure mode behaviour and to manage maintenance. Finally, a case study describes the framework using the referred template.     

扩展服务路由器操作维护系统的研究

路由器操作维护(operating and maintenance,简称OAM)系统负责对路由器进行操作和管理.它是路由器正常运行的保证,是路由器中的重要模块.随着路由器技术的发展,对路由器软件动态升级的要求越来越受到人们的重视.对扩展服务路由器操作管理进行了深入的研究,首先介绍了扩展服务路由器操作管理的设计要求和研究现状,然后介绍了清华大学研制的扩展服务路由器原型系统的软、硬件体系结构及其对操作维护系统的功能要求,设计并实现了可实时动态加载扩展服务组件的操作维护管理系统.最后指出了进一步的研究方向.     

Predictive maintenance using cox proportional hazard deep learning

Predictive maintenance (PdM) has become prevalent in the industry in order to reduce maintenance cost and to achieve sustainable operational management. The core of PdM is to predict the next failure so corresponding maintenance can be scheduled before it happens. The purpose of this study is to establish a Time-Between-Failure (TBF) prediction model through a data-driven approach. For PdM, data sparsity is regarded as a critical issue which can jeopardize algorithm performance for the modelling based on maintenance data. Meanwhile, data censoring has imposed another challenge for handling maintenance data because the censored data is only partially labelled. Furthermore, data sparsity may affect algorithm performance of existing approaches when addressing the data censoring issue. In this study, a new approach called Cox proportional hazard deep learning (CoxPHDL) is proposed to tackle the aforementioned issues of data sparsity and data censoring that are common in the analysis of operational maintenance data. The idea is to offer an integrated solution by taking advantage of deep learning and reliability analysis. To start with, an autoencoder is adopted to convert the nominal data into a robust representation. Secondly, a Cox proportional hazard model (Cox PHM) is researched to estimate the TBF of the censored data. A long-short-term memory (LSTM) network is then established to train the TBF prediction model based on the pre-processed maintenance data. Experimental studies using a sizable real-world fleet maintenance data set provided by a UK fleet company have demonstrated the merits of the proposed approach where the algorithm performance based on the proposed LSTM network has been improved respectively in terms of MCC and RMSE.     

A Case Study of Maintenance of a Commercially Fielded Case-Based Reasoning System

This article is a case study of the maintenance required to the case base of a commercially fielded case-based reasoning (CBR) system that provides support for HVAC engineers enabling them to better specify HVAC installations. The article briefly describes the system and details how the case base grew rapidly, causing a problem of case redundancy. A simple algorithm to identify and remove redundant cases is described, along with the results of applying it to the case base. Case obsolescence also was encountered and partially remedied using DBMS techniques. The article analyzes the case-base maintenance (CBM) required by the system in terms of Richter's knowledge containers and Leake and Wilson's CBM framework and contrasts this case study with experience from NEC and DaimlerChrysler. The article observes that had maintenance of the case base been considered more explicitly during system design and implementation, some of the resulting maintenance would have been unnecessary. The article concludes by identifying lessons learned and highlighting the relationship between the sophistication of the case-representation and similarity metrics and the ease with which CBM can be undertaken by nontechnical staff. This relationship does not always work in favor of the maintainer.