本文選題：預(yù)防維修　切入點(diǎn)：延遲時(shí)間　出處：《北京科技大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：生產(chǎn)設(shè)備維修管理是制造企業(yè)生產(chǎn)中一項(xiàng)重要的要素,設(shè)備一旦發(fā)生故障對(duì)企業(yè)可能會(huì)造成巨大的經(jīng)濟(jì)損失,因此生產(chǎn)設(shè)備的可靠性越來(lái)越重要。傳統(tǒng)的設(shè)備維修管理理論和方法存在諸多問(wèn)題。基于狀態(tài)維修策略是以狀態(tài)為依據(jù)的維修。該策略能夠很好的解決傳統(tǒng)維修方式的不足。本文基于延遲時(shí)間理論,結(jié)合企業(yè)維修管理中采用的實(shí)際預(yù)防維修策略,分析和解決企業(yè)在制定預(yù)防維修策略時(shí)存在的實(shí)際問(wèn)題,以降低設(shè)備故障發(fā)生率和維修成本／總停工時(shí)間為目的,展開以下四個(gè)方面的研究。主要研究?jī)?nèi)容和創(chuàng)新點(diǎn)如下：(1)在單一部件系統(tǒng)的預(yù)防維修策略研究中,考慮加入兩階段檢測(cè)策略和基于年齡預(yù)防更換策略,目的是為了降低單位時(shí)間內(nèi)期望成本。系統(tǒng)經(jīng)歷三個(gè)階段分別為正常階段、初始缺陷階段和嚴(yán)重缺陷階段。一旦系統(tǒng)在檢測(cè)點(diǎn)被識(shí)別出處于初始缺陷階段時(shí),如果該檢測(cè)點(diǎn)距離基于年齡預(yù)防更換時(shí)刻的時(shí)間間隔小于事先設(shè)定的某一閾值時(shí)則考慮對(duì)系統(tǒng)實(shí)施延遲預(yù)防維修策略,否則,系統(tǒng)立即被更新。使用蜜蜂群優(yōu)化算法求解模型獲得最優(yōu)預(yù)防維修策略。數(shù)值算例表明該模型能夠有效的降低單位時(shí)間內(nèi)期望成本,仿真算法被提出充分證明了模型構(gòu)建的正確性。(2)上述研究?jī)?nèi)容中使用蜜蜂群優(yōu)化算法求解多維決策變量預(yù)防維修模型。然而蜜蜂群優(yōu)化算法易陷入局部最優(yōu)解和精度低,因此本論文研究蜜蜂群優(yōu)化算法仿生機(jī)理,解的構(gòu)造過(guò)程及相關(guān)參數(shù)設(shè)置,從而進(jìn)一步改進(jìn)算法優(yōu)化性能。將具有全局收斂性的模擬退火算法與具有局部收斂性的蜜蜂群算法結(jié)合,提出改進(jìn)的蜜蜂群優(yōu)化算法。通過(guò)數(shù)值算例驗(yàn)證了改進(jìn)的蜜蜂群優(yōu)化算法能夠有效的求解多維決策變量預(yù)防維修模型。(3)在企業(yè)預(yù)防維修行為中在線狀態(tài)監(jiān)測(cè)和人工檢測(cè)被實(shí)施是為了識(shí)別系統(tǒng)的狀態(tài)�？紤]對(duì)單一部件系統(tǒng)聯(lián)合實(shí)施在線狀態(tài)監(jiān)測(cè)和人工檢測(cè)預(yù)防維修行為。事先設(shè)置兩個(gè)控制線：檢測(cè)閾值和預(yù)防更換閾值。當(dāng)在線狀態(tài)監(jiān)測(cè)測(cè)量值處于檢測(cè)閾值和預(yù)防更換閾值之間時(shí),實(shí)施人工檢測(cè)。一旦確認(rèn)系統(tǒng)處于延遲時(shí)間階段則更新系統(tǒng),否則不采取任何維修措施。當(dāng)在線狀態(tài)監(jiān)測(cè)測(cè)量值大于預(yù)防更換閾值時(shí),更新系統(tǒng)。系統(tǒng)經(jīng)歷兩個(gè)階段分別為正常階段和延遲時(shí)間階段。假設(shè)人工檢測(cè)是完美的。狀態(tài)監(jiān)測(cè)間隔和檢測(cè)閾值是決策變量,目的是為了最小化單位時(shí)間內(nèi)期望成本。數(shù)值算例表明所提出的預(yù)防維修模型是有效的。仿真算法充分證明了模型構(gòu)建的正確性。(4)考慮復(fù)雜系統(tǒng)的預(yù)防維修決策研究。以某鋼廠企業(yè)中的氧槍系統(tǒng)為研究對(duì)象。通過(guò)搜集和分析原始維修記錄,使用兩階段延遲時(shí)間理論構(gòu)建統(tǒng)計(jì)模型。采用最大似然方法對(duì)模型參數(shù)進(jìn)行參數(shù)估計(jì),然后考慮預(yù)防維修周期對(duì)總停工時(shí)間的影響建立預(yù)防維修模型,通過(guò)優(yōu)化該模型得到氧槍系統(tǒng)的最優(yōu)預(yù)防維修周期。建模時(shí)將所有可能出現(xiàn)的缺陷按照危險(xiǎn)程度和維修時(shí)間長(zhǎng)短分為兩類：大缺陷和小缺陷。通過(guò)赤池信息準(zhǔn)則選擇合適的分布形式,然后在通過(guò)卡方擬合優(yōu)度檢驗(yàn)方法對(duì)其進(jìn)行驗(yàn)證。模型優(yōu)化結(jié)果表明：該鋼廠企業(yè)制定的實(shí)際預(yù)防維修策略不符合最優(yōu)預(yù)防維修策略的制定。
[Abstract]:Maintenance management of equipment manufacturing is an important factor in the production of enterprises, equipment failures of enterprises may cause huge economic losses, so the reliability is more and more important. The traditional production equipment maintenance management theory and method has many problems. Based on the condition based maintenance strategy is based on the state of maintenance. The strategy can solve the shortcomings of traditional maintenance mode. This paper based on the theory of time delay, combined with the maintenance and management of the enterprise actual preventive maintenance strategy, to analyze and solve practical problems in the development of preventive maintenance strategy of enterprises, in order to reduce the equipment failure rate and maintenance cost / total downtime for the purpose of the following in the following four aspects. The main research contents and innovations are as follows: (1) in the study of preventive maintenance strategy for single components of the system, consider joining the two stage The detection strategy based on age and preventive replacement policy, the purpose is to reduce the expected cost per unit time. The system has experienced three stages: normal stage, initial stage and defect serious defect stage. Once the system is identified in the detection point comes from the initial defect stage, if the detection point from a threshold age preventive replacement time the time interval is less than the pre-set time delay based on considering the implementation of preventive maintenance strategy of the system, otherwise, the system is updated immediately. The use of bee swarm optimization algorithm to solve the model to obtain the optimal preventive maintenance strategy. Numerical examples show that the model can effectively reduce the expected cost per unit time, the simulation algorithm is proposed to fully demonstrate the correctness of model. (2) the bee swarm optimization algorithm for decision variable preventive maintenance model using the above research content. However, the bees. The algorithm is easy to fall into local optimal solution and low precision, so this thesis bee swarm optimization algorithm of bionic mechanism, set up construction process solutions and related parameters, so as to further improve the optimization performance of the algorithm. Combined with the simulated annealing algorithm has global convergence and local convergence has bee swarm algorithm, an improved bee colony optimization the algorithm is verified through a numerical example. The improved bee swarm optimization algorithm can solve the multidimensional model of preventive maintenance decision variables effectively. (3) in the prevention of enterprise online monitoring and maintenance manual detection behavior is carried out to identify the state of the system. Considering the joint implementation of preventive maintenance behavior of online monitoring and artificial detection of single component set in advance system. Two control line: the detection threshold and preventive replacement threshold. When the measured value in on-line monitoring the detection threshold and prevent more Change the threshold when the implementation of artificial detection. Once confirmed in delay time stage, update the system, otherwise do not take any maintenance measures. When updating the system of on-line monitoring the measured value is greater than the preventive replacement threshold,. The system has two stages respectively in normal phase and time delay phase. Under the assumption that the artificial detection is the perfect state. The monitoring interval and the detection threshold is the decision variables, objective is to minimize the expected cost per unit time. Numerical examples show that the proposed preventive maintenance model is effective. The simulation algorithm fully proved the correctness of the model. (4) the research of preventive maintenance decision making complex system into account. In a steel enterprise in oxygen lance system as the research object. Through the collection and analysis of original maintenance records, the use of two phase delay time theory to construct statistical models. Using the maximum likelihood method to model parameters. For parameter estimation, and then consider the preventive maintenance period of the establishment of preventive maintenance model for the effect of total downtime, the optimal preventive maintenance period of the model of oxygen lance system. When modeling all possible defects according to the degree of risk and the maintenance time is divided into two categories: large defects and small defects. Through Akaike information criteria for selection of appropriate forms of distribution, and then through the chi square goodness of fit test method to verify the model. The optimization results show that the actual preventive maintenance strategy of the steel enterprises set not in line with the development of optimal maintenance strategy.

【學(xué)位授予單位】：北京科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TB49

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