Optimal maintenance policies for systems subject to a Markovian operating environment

Many stochastic models of repairable equipment deterioration have been proposed based on the physics of failure and the characteristics of the operating environment, but they often lead to time to failure and residual life distributions that are quite complex mathematically. The first objective of our study is to investigate the potential for approximating these distributions with traditional time to failure distribution. We consider a single-component system subject to a Markovian operating environment such that the system’s instantaneous deterioration rate depends on the state of the environment. The system fails when its cumulative degradation crosses some random threshold. Using a simulation-based approach, we approximate the time to first failure distribution for this system with a Weibull distribution and assess the quality of this approximation. The second objective of our study is to investigate the cost benefit of applying a condition-based maintenance paradigm (as opposite to a scheduled maintenance paradigm) to the repairable system of interest. Using our simulation model, we assess the cost benefits resulting from condition-based maintenance policy, and also the impact of the random prognostic error in estimating system condition (health) on the cost benefits of the condition-based maintenance policy.     

状态检修专家系统中神经网络的应用研究

介绍了设备状态检修专家系统的结构，提出了一种基于模块化的神经网络的系统结构和学习算法。它通过用分解判定子模块对输入向量的适当分区域、由合成子网将各区域的结果合成，实现了复杂任务的自动分解判定和模块化训练策略。研究表明该结构和算法是可行的、有效的，它具有并行性高、对新增样本易于学习等特点。     

Remote,condition-based maintenance for web-enabled robotic system

The current trends in industry include an integration of information and knowledge-base network with a manufacturing system, which coined a new term, e-manufacturing. From the perspective of e-manufacturing any production equipment and its control functions do not exist alone, instead becoming a part of the holistic operation system with distant monitoring, remote quality control, and fault diagnostic capabilities. The key to this new paradigm is the accessibility to a remotely located system and having the means of responding to a changing environment, which is better suited for today's rapidly changing environment. Within the framework of the web-enabled robotic system, this paper focuses on the remote maintenance schemes with an emphasis on condition-based maintenance strategies. Real-time monitoring of robot harmonic drive systems and operational status have been attained over the Web. A mathematical modeling of system availability has been derived in order to account for other failures that might occur in the subsystems of the robot. Compared to the schedule-based maintenance strategies, the proposed approach shows great potential for improving overall production efficiency, while reducing the cost of maintenance.     

计算机辅助电力设备故障模式与影响分析方法

以近年来我国电力企业设备检修实践为基础,从状态检修管理体制、人员素质和技术检测手段等几个方面阐述了在向状态检修模式迈进的历程中所采取的措施、设想和面临的困难及解决的思路,并论述了计算机技术在其中的应用。     

某型战车信息终端仿真器的设计

针对装备维修训练中存在的装备结构复杂,价格昂贵,维修训练成本高的问题;设计了一个用于装备维修训练的信息终端仿真器;以某型战车驾驶员任务终端的内部结构、工作原理、信号特征和维修流程为依据,采用仿真技术、嵌入式设计技术等,重新设计与实物相似的终端仿真器,并以该仿真器为核心,辅以信号发生器及上位机等软硬件设备构造用于维修训练的仿真平台;该仿真器可进行信息终端的内部构造原理认知、维修过程训练和训练结果评估,应用表明该仿真器可满足装备维修教学和培训需求,提高维修人员的维修技能水平。     

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.     

An overview of methods to evaluate uncertainty of deterministic models in decision support

There is an increasing need for environmental management advice that is wide-scoped, covering various interlinked policies, and realistic about the uncertainties related to the possible management actions. To achieve this, efficient decision support integrates the results of pre-existing models. Many environmental models are deterministic, but the uncertainty of their outcomes needs to be estimated when they are utilized for decision support. We review various methods that have been or could be applied to evaluate the uncertainty related to deterministic models' outputs. We cover expert judgement, model emulation, sensitivity analysis, temporal and spatial variability in the model outputs, the use of multiple models, and statistical approaches, and evaluate when these methods are appropriate and what must be taken into account when utilizing them. The best way to evaluate the uncertainty depends on the definitions of the source models and the amount and quality of information available to the modeller.     

基于虚拟现实的军用后勤装备维修训练系统模型设计

通过研究分析虚拟现实理论和特点,结合后勤装备维修训练系统要素和具体要求,探索一种适应部队信息化建设发展的数字化维修训练系统,即基于虚拟现实的后勤装备虚拟维修训练系统。从系统模型组织结构入手,深入分析了各层次内涵,为建立具体装备的虚拟维修训练系统提供了理论基础。     

虚拟维修技术及其在装备维修上的应用

虚拟维修是虚拟现实技术在维修领域的具体应用.文章介绍了虚拟维修的定义和虚拟维修的特点,探讨了在装备维修训练上的应用.     

虚拟维修技术及其在装备维修上的应用

虚拟维修是虚拟现实技术在维修领域的具体应用。文章介绍了虚拟维修的定义和虚拟维修的特点，探讨了在装备维修训练上的应用。     

基于HMM的设备剩余寿命预测框架及其实现

在设备退化状态识别中剩余寿命预测是设备实施状态维修的一个重要内容,也是实现真正意义上精确维修的前提和基础。隐马尔可夫模型作为一种统计分析算法,具有较强的模式分类能力,能够对设备退化过程进行有效识别。但是传统隐马尔可夫模型状态持续时间分布的不合理性,不能直接用于剩余寿命的预测。考虑状态识别和剩余寿命预测的连续性,应用隐马尔可夫模型的改进算法——隐半马尔可夫模型,构建设备剩余寿命的预测框架,实现了在设备退化状态识别基础上的剩余寿命预测。通过对滚动轴承实测数据的仿真试验,对框架进行了仿真验证。仿真结果表明框架能够有效识别设备退化过程和实现剩余寿命的预测。     

企业离散式智能设备预测性维护综述

针对智能设备的大量使用且缺乏根据监测大数据进行故障自动分析、判断与处理的问题,研究了基于物联网技术、大数据技术、边云协同技术的智能设备预测性维护框架和模式.提出针对非智能设备安装传感器实现设备智能化的方法.指出边缘计算负责设备工况数据的实时采集、分析,可快速甄别设备故障并实时报警;云计算聚焦同类设备运行海量历史数据的挖...     

TN8000水电机组状态监测分析故障诊断系统

概略地介绍了TN8000水电机组状态监测分析故障诊断系统的作用及适用范围,画出了系统的网络构成图,详细介绍了系统的各种功能,给出了该系统在实际工程中的应用实例。     

Combining intensional with extensional query evaluation in tuple independent probabilistic databases

In this paper, we prove that a query plan is safe in tuple independent probabilistic databases if and only if its every answer tuple is tree structured in probabilistic graphical models. We classify hierarchical queries into core and non-core hierarchical queries and show that the existing methods can only generate safe plans for core hierarchical queries. Inspired by the bucket elimination framework, we give the sufficient and necessary conditions for the answer relation of every candidate sub-query to be used as a base relation. Finally, the proposed algorithm generates safe plans for extensional query evaluation on non-boolean hierarchical queries and invokes the SPROUT algorithm [24] for intensional query evaluation on boolean queries. A case study on the TPC-H benchmark reveals that the safe plans of Q7 and Q8 can be evaluated efficiently. Furthermore, extensive experiments show that safe plans generated by the proposed algorithm scale well.     

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.