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.     

Competence Models and the Maintenance Problem

Case-based reasoning (CBR) systems solve problems by retrieving and adapting the solutions to similar problems that have been stored previously as a case base of individual problem solving episodes or cases. The maintenance problem refers to the problem of how to optimize the performance of a CBR system during its operational lifetime. It can have a significant impact on all the knowledge sources associated with a system (the case base, the similarity knowledge, the adaptation knowledge, etc.), and over time, any one, or more, of these knowledge sources may need to be adapted to better fit the current problem-solving environment. For example, many maintenance solutions focus on the maintenance of case knowledge by adding, deleting, or editing cases. This has lead to a renewed interest in the issue of case competence, since many maintenance solutions must ensure that system competence is not adversely affected by the maintenance process. In fact, we argue that ultimately any generic maintenance solution must explicitly incorporate competence factors into its maintenance policies. For this reason, in our work we have focused on developing explanatory and predictive models of case competence that can provide a sound foundation for future maintenance solutions. In this article we provide a comprehensive survey of this research, and we show how these models have been used to develop a number of innovative and successful maintenance solutions to a variety of different maintenance problems.     

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.     

Maintaining Case-Based Reasoners: Dimensions and Directions

Experience with the growing number of large-scale and long-term case-based reasoning (CBR) applications has led to increasing recognition of the importance of maintaining existing CBR systems. Recent research has focused on case-base maintenance (CBM), addressing such issues as maintaining consistency, preserving competence, and controlling case-base growth. A set of dimensions for case-base maintenance, proposed by Leake and Wilson, provides a framework for understanding and expanding CBM research. However, it also has been recognized that other knowledge containers can be equally important maintenance targets. Multiple researchers have addressed pieces of this more general maintenance problem, considering such issues as how to refine similarity criteria and adaptation knowledge. As with case-base maintenance, a framework of dimensions for characterizing more general maintenance activity, within and across knowledge containers, is desirable to unify and understand the state of the art, as well as to suggest new avenues of exploration by identifying points along the dimensions that have not yet been studied. This article presents such a framework by (1) refining and updating the earlier framework of dimensions for case-base maintenance, (2) applying the refined dimensions to the entire range of knowledge containers, and (3) extending the theory to include coordinated cross-container maintenance. The result is a framework for understanding the general problem of case-based reasoner maintenance (CBRM). Taking the new framework as a starting point, the article explores key issues for future CBRM research.     

An optimization algorithm inspired by propagation of yeast for fleet maintenance decision making problem involving fatigue structures

To minimize airline maintenance costs and maximize fleet availability, we developed a fleet maintenance decision-making model based on CBM with collaborative optimization (CO) for fatigue structures. The model is divided into two levels: a system level and a subsystem level. Different optimization routines are used at these two levels. The system level focuses on maximizing fleet availability and the subsystem level focuses on minimizing aircraft maintenance costs. Moreover, we proposed an optimization algorithm inspired by the propagation of yeast (OA/PY) to handle the situation where optimal solution is not unique. Finally, a case study regarding a fleet of 10 aircrafts is conducted, and the results demonstrated the effectiveness of the proposed algorithm. In the case study, aircraft maintenance planning (subsystem level) was obtained, and then it was adjusted with OA/PY to obtain optimal fleet maintenance plan (system level). Total incremental maintenance cost caused by the adjustment in the proposed method was reduced by 70.65%.     

复杂系统维护策略最新研究进展: 从视情维护到预测性维护

对于复杂、可修复的工程系统, 设备维护是确保系统安全性、可靠性、可用性的重要手段之一. 系统维护策略已经历修复性维护、定时维护、视情维护等多种维护策略. 其中, 视情维护是目前最受关注的维护策略, 它通过收集和评估系统的实时状态信息进行维护决策, 具有全寿命周期内系统可靠性高、运营维护成本低等优点. 近年来, 随着物联网技术、信息技术和人工智能的快速发展, 一种更新颖的视情维护策略——预测性维护逐渐成为领域研究热点. 本文首先简要回顾了系统维护策略的发展历程; 然后, 重点介绍了视情维护的研究进展, 根据决策支持技术的不同, 将视情维护划分为基于随机退化模型的视情维护和基于数据驱动的预测性维护, 对每类技术的发展分支与研究现状进行了疏理、分析和总结; 最后, 探讨了当前复杂系统维护策略面临的挑战性问题和可能的未来研究方向.     

Maintaining Retrieval Knowledge in a Case-Based Reasoning System

The knowledge stored in a case base is central to the problem solving of a case-based reasoning (CBR) system. Therefore, case-base maintenance is a key component of maintaining a CBR system. However, other knowledge sources, such as indexing and similarity knowledge for improved case retrieval, also play an important role in CBR problem solving. For many CBR applications, the refinement of this retrieval knowledge is a necessary component of CBR maintenance. This article focuses on optimization of the parameters and feature selections/weights for the indexing and nearest-neighbor algorithms used by CBR retrieval. Optimization is applied after case-base maintenance and refines the CBR retrieval to reflect changes that have occurred to cases in the case base. The optimization process is generic and automatic, using knowledge contained in the cases. In this article we demonstrate its effectiveness on a real tablet formulation application in two maintenance scenarios. One scenario, a growing case base, is provided by two snapshots of a formulation database. A change in the company's formulation policy results in a second, more fundamental requirement for CBR maintenance. We show that after case-base maintenance, the CBR system did indeed benefit from also refining the retrieval knowledge. We believe that existing CBR shells would benefit from including an option to automatically optimize the retrieval process.     

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。     

Transferring Case Knowledge To Adaptation Knowledge: An Approach for Case-Base Maintenance

In this article we propose a case-base maintenance methodology based on the idea of transferring knowledge between knowledge containers in a case-based reasoning (CBR) system. A machine-learning technique, fuzzy decision-tree induction, is used to transform the case knowledge to adaptation knowledge. By learning the more sophisticated fuzzy adaptation knowledge, many of the redundant cases can be removed. This approach is particularly useful when the case base consists of a large number of redundant cases and the retrieval efficiency becomes a real concern of the user. The method of maintaining a case base from scratch, as proposed in this article, consists of four steps. First, an approach to learning feature weights automatically is used to evaluate the importance of different features in a given case base. Second, clustering of cases is carried out to identify different concepts in the case base using the acquired feature-weights knowledge. Third, adaptation rules are mined for each concept using fuzzy decision trees. Fourth, a selection strategy based on the concepts of case coverage and reachability is used to select representative cases. In order to demonstrate the effectiveness of this approach as well as to examine the relationship between compactness and performance of a CBR system, experimental testing is carried out using the Traveling and the Rice Taste data sets. The results show that the testing case bases can be reduced by 36 and 39 percent, respectively, if we complement the remaining cases by the adaptation rules discovered using our approach. The overall accuracies of the two smaller case bases are 94 and 90 percent, respectively, of the originals.     

复杂装备故障预测与健康管理体系结构研究

PHM是基于CBM概念的一种新技术,对提高复杂装备的维修保障能力和减少维修保障费用有着重大意义;文章以复杂装备为研究对象,针对其PHM系统设计的首要问题———PHM体系结构进行了探索,提出了适合复杂装备的PHM总体结构和相应的硬件、软件系统结构,并在此基础上结合现行的三级维护体制提出了基于Web的防空导弹装备PHM总体结构。     

Flexible job shop scheduling under condition-based maintenance: Improved version of imperialist competitive algorithm

Maintenance activities have been ignored in many studies on scheduling problems where all machines are assumed to be available without interruption in the planning horizon. However, in realistic situations, they might be unavailable due to preventive maintenance, basic maintenance or unforeseen breakdowns. In this paper, we simulate a condition-based maintenance (CBM) for flexible job shop scheduling problem (FJSP) and consider the combination of Sigmoid function and Gaussian distribution to improve the CBM simulation. This study proposes an improved imperialist competitive algorithm (ICA) for the FJSP scheduling problem with the objective of the makespan minimization. The performance of the proposed algorithm is enhanced with a hybridization of ICA with simulated annealing (SA), after diagnosing standard ICA disadvantages and shortcomings. This ICA also includes a simulation part to handle CBM requirements. Various parameters of the novel ICA are reviewed to calibrate the algorithm with the help of the Taguchi experimental design. Experimental results show the high performance of the novel ICA in comparison with the standard ICA. The obtained results demonstrate that the novel ICA is an effective algorithm for FJSP under CBM. Finally, the performance of ICA is evaluated compared to other popular algorithms.     

分形技术在案例库维护中的应用

阐述了分形技术运用于案例库维护的可行性,并提出了一种基于分形技术的案例库维护模型,该模型利用分形技术中的盒维算法对案例库进行维护。实验表明该模型能够较为明显地降低案例库的规模,增强案例库数据全局分布的均匀性,进而提高案例库的检索效率。     

A Case-Addition Policy for Case-Base Maintenance

A major problem in many practical applications of case-based reasoning (CBR) and knowledge reuse is how to keep the case bases concise and complete. To solve this problem requires repeated maintenance operations to be applied to case bases. Different maintenance policies may result in case bases with very different quality. In this article, we present a case-addition maintenance policy that is guaranteed to return a concise case base with good coverage quality. We demonstrate that the coverage of the case base computed by the case-addition algorithm is no worse than the optimal case-base coverage by a fixed lower bound. We also show that the algorithm implementing the case-addition policy is efficient. Our result also highlights benefit reduction as a key factor in influencing the convergence of case-base coverage when cases are added to a case base. Through our theoretical analysis, we analytically derive the well known coverage convergence curves commonly displayed in CBR experiments and show that benefit reduction can be used as a predictor for convergence speed.     

An intelligent condition-based maintenance platform for rotating machinery

Maintenance is of necessity for sustaining machinery availability and reliability in order to ensure productivity, product quality, on-time delivery, and safe working environment. The costly maintenance strategies such as corrective maintenance and scheduled maintenance have been progressively replaced by superior maintenance strategies in which condition-based maintenance (CBM) is one of the delegates. This strategy commonly consists of sequent modules such as data acquisition, signal processing, feature extraction and feature selection, condition monitoring, etc. However, approaches in literature which have been developed for each module and implemented for different applications are standalone instead of a comprehensive system. Furthermore, these approaches have been demonstrated in a laboratory environment without any industrial validations. For these reasons, an intelligent algorithm based CBM platform is proposed in this paper to be applied for rotating machinery easily and effectively. Subsequently, two case-studies are presented in order to evaluate the effectiveness of this platform in industrial applications.     

Distributing Case-Base Maintenance: The Collaborative Maintenance Approach

Case-base maintenance is an important emerging issue for case-base reasoners as they scale up to handle real-world problems in unstable environments. Over time, the contents of a case base may become out of date or inconsistent with the target problem space, and maintenance strategies must be devised to help recognize and repair these problems by deleting, adding, or modifying cases, for example. In this article we describe a maintenance framework called collaborative maintenance (CM), which has been designed to facilitate, monitor, and control the incremental update of live, dynamic case bases. Our approach is novel in that it automatically supports a distributed, interactive maintenance process—users are permitted to recommend case updates, and the collaborative maintenance process ensures that these recommendations are properly reviewed and actioned.     

视情维修条件下舰载机的战备完好性

基于视情维修方法的维修系统是复杂的动力学系统,其不同构成要素之间的相互作用导致维修系统的动态变化。通过分析视情维修的内涵,以舰载机的战备完好性为目标,运用系统动力学（SD）方法,并应用专用软件VENSIM分析视情维修条件下舰载机维修保障系统内部各要素的反馈控制结构,建立视情维修条件下舰载机战备完好性的系统动力学模型。仿真模型的运行结果表明,将视情维修方法引入舰载机维修保障,可有效提高舰载机的战备完好性     

一种基于相似粗糙集技术的案例库维护

最近的研究工作突现了在案例推理过程中案例库维护的重要性,越来越多的人认为基于案例推理系统包含了案例库维护的有关过程(Review和Restore)。案例库维护作为CBR研究的一个分支,已经研究出不同的案例库维护策略,其中一些是限制案例库的规模,由此引发了CBR系统的能力与效率问题。相似粗糙集技术可以有效地利用差别矩阵,通过不同的相似度阈值发现以及处理案例库的冗余,有选择地删除多余的案例;同时案例库的覆盖度不降低,减少了案例适应性修改的代价,从而确保了CBR系统的能力与效率的兼顾。     

基于图论的建筑机电设备逻辑关系自动提取方法

随着建筑信息模型(BIM)在建筑施工及运维阶段的深入应用,建筑机电设备的逻辑 关系自动提取成为进一步应用的瓶颈。针对建筑机电系统信息模型应用过程中逻辑连接关系应 用需求高、判断复杂的问题,提出了一种基于图论的建筑机电设备逻辑关系自动提取方法。基 于 BIM,将机电系统抽象为无向连通图,连接器抽象为图的边,机电设备、管道、管道附件等 抽象为图的节点,将一片管道抽象为管道团,将设备与大量管道的复杂连接转换为设备到几个 管道团的简单连接,从而将机电系统逻辑关系自动生成的问题转换为无向连通图求解的问题, 建立了机电构件物理连接关系提取方法、设备逻辑连接关系自动生成和设备连接路径计算方法, 实现了建筑机电系统逻辑关系快速、准确、智能的提取。该方法在工程中的实施有利于基于 BIM 的机电系统运营维护管理,有利于实现建筑的全生命期信息管理。     

Deep learning forecasting for electric demand applications of cooling systems in buildings

This paper presents the application of a deep learning based model for the short-term forecasting of the electric demand in a heating, ventilation, and air conditioning system (HVAC) for the demand response programs of utility companies. The deep learning model is applied through two different approaches comparing their merits. The approaches consist of: (i) a monolithic approach that applies a single large model to forecast the target variables, and (ii) a sequential approach that consists of multiple deep learning models coupled together each targeting a specific energy load within the HVAC system. The model accuracy of both approaches is explored over two case studies applied to the same institutional building; however, the case studies differ in their data source. The first case study uses synthetic data obtained from an eQuest simulation, while the second case study uses measurement data obtained from the building automation system. Results show that the difference in forecasting error of these approaches is negligible; however, the monolithic approach required the least amount of calibration time. Next, this paper explores the application of off-site weather data applied to a building model calibrated with on-site data. The experiments demonstrated that the off-site weather data can be applied with a slight reduction in forecasting performance.     

Optimal maintenance policy incorporating system level and unit level for mechanical systems

The study works on a multi-level maintenance policy combining system level and unit level under soft and hard failure modes. The system experiences system-level preventive maintenance (SLPM) when the conditional reliability of entire system exceeds SLPM threshold, and also undergoes a two-level maintenance for each single unit, which is initiated when a single unit exceeds its preventive maintenance (PM) threshold, and the other is performed simultaneously the moment when any unit is going for maintenance. The units experience both periodic inspections and aperiodic inspections provided by failures of hard-type units. To model the practical situations, two types of economic dependence have been taken into account, which are set-up cost dependence and maintenance expertise dependence due to the same technology and tool/equipment can be utilised. The optimisation problem is formulated and solved in a semi-Markov decision process framework. The objective is to find the optimal system-level threshold and unit-level thresholds by minimising the long-run expected average cost per unit time. A formula for the mean residual life is derived for the proposed multi-level maintenance policy. The method is illustrated by a real case study of feed subsystem from a boring machine, and a comparison with other policies demonstrates the effectiveness of our approach.