维修工程管理研究与发展综述

综述了近几十年来维修工程管理的研究与发展，如人工智能、故障诊断、机器状态监测技术(如振动分析、红外检测、热录像仪等)、预测和预防性维修(PPM)、全员生产维修(TPM)和主动性维修(PM)等，论述了综合应用机器实时状态监测与故障诊断、人工智能、计算机通讯技术以及先进的维修管理理念的集成质量控制与维修系统，最后提出一种目前世界领先的远程智能维修系统。内容包括：实践应用中的维修管理评估、智能和集成维修管理、状态监测维修中的智能预测决策支持系统(IPDSS)、设备状况衰退趋势预测——人工神经网络方法、IPDSS支持的维修管理、故障诊断中的人工智能应用、基于可靠性的预防性维修安排和远程智能维修系统。     

Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system

The importance of the maintenance function has increased because of its role in keeping and improving system availability and safety, as well as product quality. To support this role, the maintenance concept has undergone several major developments that have led to proactive considerations mainly based on a prognosis process, which normally allows selection of the best maintenance action to be carried out. This paper proposes the deployment and experimentation of a prognosis process within an e-maintenance architecture. The deployment follows a methodology based on the combination of both a probabilistic approach for modelling the degradation mechanism and of an event one for dynamical degradation monitoring. The feasibility and benefits of this new prognosis process is investigated with an experiment using a manufacturing TELMA (TELe-MAintenance) platform supporting the unwinding of metal bobbins.     

Fuzzy belief propagation in constrained Bayesian networks with application to maintenance decisions

Bayesian networks have been widely applied to domains such as medical diagnosis, fault analysis, and preventative maintenance. In some applications, because of insufficient data and the complexity of the system, fuzzy parameters and additional constraints derived from expert knowledge can be used to enhance the Bayesian reasoning process. However, very few methods are capable of handling the belief propagation in constrained fuzzy Bayesian networks (CFBNs). This paper therefore develops an improved approach which addresses the inference problem through a max-min programming model. The proposed approach yields more reasonable inference results and with less computational effort. By integrating the probabilistic inference drawn from diverse sources of information with decision analysis considering a decision-maker's risk preference, a CFBN-based decision framework is presented for seeking optimal maintenance decisions in a risk-based environment. The effectiveness of the proposed framework is validated based on an application to a gas compressor maintenance decision problem.     

Optimal Bayesian maintenance policy for a gearbox subject to two dependent failure modes

Most maintenance optimization models of gear systems have considered single failure mode. There have been very few papers dealing with multiple failure modes, considering mostly independent failure modes. In this paper, we present an optimal Bayesian control scheme for early fault detection of the gear system with dependent competing risks. The system failures include degradation failure and catastrophic failure. A three‐state continuous‐time–homogeneous hidden Markov model (HMM), namely the model with unobservable healthy and unhealthy states, and an observable failure state, describes the deterioration process of the gear system. The condition monitoring information as well as the age of the system are considered in the proposed optimal Bayesian maintenance policy. The objective is to maximize the long‐run expected average system availability per unit time. The maintenance optimization model is formulated and solved in a semi‐Markov decision process (SMDP) framework. The posterior probability that the system is in the warning state is used for the residual life estimation and Bayesian control chart development. The prediction results show that the mean residual lives obtained in this paper are much closer to the actual values than previously published results. A comparison with the Bayesian control chart based on the previously published HMM and the age‐based replacement policy is given to illustrate the superiority of the proposed approach. The results demonstrate that the Bayesian control scheme with two dependent failure modes can detect the gear fault earlier and improve the availability of the system.     

Imperfect Preventive Maintenance Policy for Complex Systems Based on Bayesian Networks

The main challenge in maintenance planning lies in the realistic modeling of the maintenance policy. This paper is focused on the maintenance optimization of complex repairable systems using Bayesian networks. A new policy is developed for periodic imperfect preventive maintenance policy with minimal repair at failure; this policy allows us to take into consideration several types of preventive maintenance with different efficiency levels. The Bayesian networks are used for complex system modeling, allowing the evaluation of the model parameters. The Weibull parameters and the maintenance efficiency are evaluated thanks to the proposed methodology using Bayesian inference. The approach developed in this paper is applied on a real system, to determine the optimal maintenance plan for a turbo‐pump in oil industry. Copyright © 2016 John Wiley & Sons, Ltd.     

Vibration Monitoring and Diagnosis of Pipe-line Pumps on Offshore Platforms

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The Diagnosis of Reciprocating Machinery by Bayesian Networks

A Bayesian Network is a reasoning tool based on probability theory and has many advantages that other reasoning tools do not have,This paper discusses the basic theory of Bayesian networks and studies the problems in constructing Bayesian networks.The paper also consturcts a Bayesian diagnosis network of a reciprocating compressor.The example helps us to draw a conclusion that Bayesian diagnosis networks can diagnose reciprocating machinery effectively.     

Sensibility of Bayesian inference methods for reliability prediction of ageing systems,case of Diesel locomotives

This paper addresses the problem of reliability analysis of in-service identical systems when a limited number of lifetime data is available compared to censored ones. Lifetime (resp. censored) data characterise the life of failed (resp. non-failed) systems in the sample. Because, censored data induce biassed estimators of reliability model parameters, a methodology approach is proposed to overcome this inconvenience and improve the accuracy of the parameter estimation based on Bayesian inference methods. These methods combine, in an effective way, system’s life data and expert opinions learned from failure diagnosis of similar systems. Three Bayesian inference methods are considered: Classical Bayesian, Extended Bayesian and Bayesian Restoration Maximisation methods. Given a sample of lifetime data, simulated according to prior opinions of maintenance expert, a sensibility analysis of each Bayesian method is performed. Reliability analysis of critical subsystems of Diesel locomotives is established under the proposed methodology approach. The relevance of each Bayesian inference methods with respect to collected reliability data of critical subsystems and expert opinions is discussed.     

An Embedded Condition Monitoring and Fault Diagnosis System for Rotary Machines

An intelligent machine is the earnest aspiration of people. From the point of view to construct an intelligent machine with self-monitoring and self-diagnosis abilities, the technology for realizing an internet oriented embedded intelligent condition monitoring and fault diagnosis system for the rotating machine with remote monitoring, diagnosis, maintenance and upgrading functions is introduced systematically. Based on the DSP （ Digital Signal Processor） and embedded microcomputer, the system can measure and store the machine work status in real time, such as the rotating speed and vibration, etc. In the system, the DSP chip is used to do the fault signal processing and feature extraction, and the embedded microcomputer with a customized Linux operation system is used to realize the internet oriented remote software upgrading and system maintenance. Embedded fault diagnosis software based on mobile agent technology is also designed in the system, which can interconnect with the remote fault diagnosis center to realize the collaborative diagnosis. The embedded condition monitoring and fault diagnosis technology proposed in this paper will effectively improve the intelligence degree of the fault diagnosis system.     

机泵群实时监测网络和故障诊断专家系统

应用现代信息技术和人工智能实施设备诊断工程,逐步实现状态维修和预知维修,是大型流程工业企业降低生产成本的重要途径之一。概要介绍为实现这一目标所开发的机电装备实时监测网络和人工智能诊断技术。简要介绍了基于Ethernet和FDDI开发、应用于石化企业的机、泵群实时监测网络;首次提出了黑灰白集合筛选法,在一次原因分析法和故障机理及其识别特征研究基础上,应用此方法开发的基于黑灰白集合筛选法的机械故障诊断专家系统,用于工程实践取得了满意的结果。     

Performance updating of concrete bridges using proactive health monitoring methods

Uncertainties associated with modelling of deteriorating bridges strongly affect management decisions, such as inspection, maintenance and repair actions. These uncertainties can be reduced by the effective use of health monitoring systems, through which information regarding in situ performance can be incorporated in the management of bridges.The objectives of this paper are twofold; first, an improved chloride induced deterioration model for concrete bridges is proposed that can quantify degradation in performance soon after chlorides are deposited on the bridge, rather than when initiation of corrosion at the reinforcement level takes place. As a result, the implications of introducing proactive health monitoring can be assessed using probabilistic durability criteria. Thus, the second objective of the paper is to present a methodology for performance updating of deteriorating concrete bridges fitted with a proactive health monitoring system.This methodology is illustrated via a simple example of a typical bridge element, such as a beam or a part of a slab. The results highlight the benefits from introducing ‘smart’ technology in managing bridges subject to deterioration, and quantify the reduction in uncertainties and their subsequent effect on predictions of future bridge performance.     

一种新的建立磨损状态判别标准的方法

针对机器设备磨损故障中由于采样数据样本较少,从而故障不易诊断的情况,提出了一种基于极大熵原理的磨损状态判别标准的方法。首先利用信息论中的极大熵原理对机器设备的油液分析数据进行处理,得到油样监测数据的最优无偏估计。利用油样监测数据的无偏估计得到它的算术均值和方差,然后结合三线值法得到基于极大熵原理的磨损状态判别标准（正常值、警告值、危险值）。最后以某石化企业厂鼓风机的监测为例,分析油样铁谱数据,建立故障状态判别标准,得到初步的判别结果,并与实际的大修结果进行比较,发现所建立的方法是有效的。     

Utility based maintenance analysis using a Random Sign censoring model

Industrial systems subject to failures are usually inspected when there are evident signs of an imminent failure. Maintenance is therefore performed at a random time, somehow dependent on the failure mechanism. A competing risk model, namely a Random Sign model, is considered to relate failure and maintenance times. We propose a novel Bayesian analysis of the model and apply it to actual data from a water pump in an oil refinery. The design of an optimal maintenance policy is then discussed under a formal decision theoretic approach, analyzing the goodness of the current maintenance policy and making decisions about the optimal maintenance time.     

Maintenance and Condition-Based Maintenance

1IntroductionSystem reliability and availability have been widely studied in the literature because of their preva-lence in industry systems. Maintaining a high or required level of reliability and availability is oftenan essential requisite.Up to the 1950′s a fix it when it breaks′philosophy (BM, break maintenance) was mostly imple-mented due to lack of engineering knowledge in certain areas. The regular inspection regime of time-based maintenance (PM, preventive maintenance) was later in…     

加筋复合材料结构分层损伤的贝叶斯诊断及预测

针对复合材料结构疲劳损伤的在线监测和预测问题,提出了一种基于结构健康监测 (Structural health monitoring, SHM) 和贝叶斯理论的结构分层损伤诊断及结构剩余使用寿命预测方法。在贝叶斯概率理论框架下,采用指数模型描述复合材料结构疲劳分层损伤面积的先验演化规律,融合在线SHM数据对结构分层损伤状态,以及损伤面积演化模型的参数进行联合后验估计,即为损伤诊断结果。进一步通过后验估计得到的损伤状态和模型参数预测未来时刻结构分层损伤面积的演化,从而得到当前复合材料结构的剩余使用寿命预测结果。通过有限元仿真的加筋复合材料结构疲劳分层扩展对所提出的方法进行了验证。结果表明,方法可以在线准确地诊断结构分层损伤状态以及预测结构的剩余使用寿命。      

Research on the Correlation Between Oil Menitoring and Vibration Monitoring in Information Collecting and Processing Monitoring

Oil monitoring and vibration monitoring are two principal techniques for mechanical fault diagnosis and condition monitoring at present. They monitor the mechanical condition by different approaches, nevertheless, oil and vibration monitoring are related in information collecting and processing. In the same mechanical system, the information obtained from the same information source can be described with the same expression form. The expressions are constituted of a structure matrix, a relative matrix and a system matrix. For oil and vibration monitoring, the information source is correlation and the collection is independent and complementary. And oil monitoring and vibration monitoring have the same process method when they yield their information. This research has provided a reasonable and useful approach to combine oil monitoring and vibration monitoring.     

Bayesian monitoring of linear profile monitoring using DEWMA charts

Process monitoring is an essential element for an improved quality of final products. A variety of tools are used for it; control charts are one of these choices. Classical and Bayesian thoughts are 2 main aspects of statistics used in different areas of application. This study introduces an approach to existing theories in applied quality control: Bayesian double exponentially weighted moving average (DEWMA) control charts for monitoring the profiles of products and processes. Three novel univariate Bayesian DEWMA charting structures for the Y intercepts, slopes, and error variances are designed under phase 2 procedures. The performance of the designed structures of control charts is evaluated based on different run length measures. The comparative analysis revealed that Bayesian DEWMA control charts are efficient at identifying the sustainable shifts in the process parameters. Moreover, DEWMA control charts are more effective under classical and Bayesian methodologies for detecting smaller value shifts compared with exponentially weighted moving average charts. We have examined that acquiring extra information in the form of prior's about process parameters comes up with tangible benefits and enhances the detection potential of DEWMA charts for profiles monitoring. An example and case studies are provided to justify the above findings.     

Bayesian Diagnostic Network： A Powerful Model for Representation and Reasoning of Engineering Diagnostic Knowledge

Engineering diagnosis is essential to the operation of industrial equipment. The key to successful diagnosis is correct knowledge representation and reasoning. The Bayesian network is a powerful tool for it. This paper utilizes the Bayesian network to represent and reason diagnostic knowledge, named Bayesian diagnostic network. It provides a three-layer topologic structure based on operating conditions, possible faults and corresponding symptoms. The paper also discusses an approximate stochastic sampling algorithm. Then a practical Bayesian network for gas turbine diagnosis is constructed on a platform developed under a Visual C environment. It shows that theBayesian net work is a powerful model for representation and reasoning of diagnostic knowledge. The three-layer structure and the approximate algorithm are effective also.     

Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN)

Nowadays, the complex manufacturing processes have to be dynamically modelled and controlled to optimise the diagnosis and the maintenance policies. This article presents a methodology that will help developing Dynamic Object Oriented Bayesian Networks (DOOBNs) to formalise such complex dynamic models. The goal is to have a general reliability evaluation of a manufacturing process, from its implementation to its operating phase. The added value of this formalisation methodology consists in using the a priori knowledge of both the system's functioning and malfunctioning. Networks are built on principles of adaptability and integrate uncertainties on the relationships between causes and effects. Thus, the purpose is to evaluate, in terms of reliability, the impact of several decisions on the maintenance of the system. This methodology has been tested, in an industrial context, to model the reliability of a water (immersion) heater system.     

Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN)

Nowadays, the complex manufacturing processes have to be dynamically modelled and controlled to optimise the diagnosis and the maintenance policies. This article presents a methodology that will help developing Dynamic Object Oriented Bayesian Networks (DOOBNs) to formalise such complex dynamic models. The goal is to have a general reliability evaluation of a manufacturing process, from its implementation to its operating phase. The added value of this formalisation methodology consists in using the a priori knowledge of both the system's functioning and malfunctioning. Networks are built on principles of adaptability and integrate uncertainties on the relationships between causes and effects. Thus, the purpose is to evaluate, in terms of reliability, the impact of several decisions on the maintenance of the system. This methodology has been tested, in an industrial context, to model the reliability of a water (immersion) heater system.