【摘要】：設(shè)備的最優(yōu)維修問題維修問題一直是企業(yè)長(zhǎng)久以來關(guān)注和研究的問題之一。隨著科學(xué)技術(shù)的快速發(fā)展,雖然設(shè)備在安全性和可靠性方面都有了很大程度的提高,但是設(shè)備的維修成本并沒有明顯降低。研究發(fā)現(xiàn),維修過程中的人因失誤是導(dǎo)致成本費(fèi)用率升高的重要原因之一。特別是企業(yè)設(shè)備的數(shù)量、種類的增多、規(guī)模的擴(kuò)大、復(fù)雜性程度的提高,對(duì)設(shè)備維護(hù)人員提高相應(yīng)的專業(yè)知識(shí)和技能有了新的要求。實(shí)際中,維護(hù)人員的專業(yè)知識(shí)和技能與設(shè)備的更新速度差距較大,導(dǎo)致維修人員差錯(cuò)率不斷上升,這表明人因因素對(duì)設(shè)備狀態(tài)檢測(cè)及維修效果的影響越來越明顯。因而,考慮人因因素對(duì)狀態(tài)檢測(cè)的影響,方可制定出更合理的維修方案。本文將“人因因素”引入到設(shè)備視情維修決策優(yōu)化過程中,研究了其建模與優(yōu)化問題,主要內(nèi)容包括:首先,利用功能共振模型,以視情維修為研究對(duì)象,將其分為五個(gè)功能單元,建立功能共振網(wǎng)絡(luò),對(duì)該過程中可能存在的人因因素進(jìn)行分析。研究發(fā)現(xiàn)從數(shù)據(jù)收集到?jīng)Q策的制定,人因失誤主要發(fā)生在數(shù)據(jù)收集和診斷檢測(cè)環(huán)節(jié)。然后,將視情維修中識(shí)別出的人因失誤因素進(jìn)行定量分析,通過層次分析法與成功似然法計(jì)相結(jié)合的方法以計(jì)算診斷檢測(cè)環(huán)節(jié)失誤發(fā)生的概率并分析其致因是由于相關(guān)人員經(jīng)驗(yàn)、專業(yè)技能和知識(shí)缺乏導(dǎo)致。最后,以單設(shè)備系統(tǒng)為研究對(duì)象,考慮人因因素對(duì)其進(jìn)行最優(yōu)維修決策建模與優(yōu)化研究。在對(duì)設(shè)備的劣化狀態(tài)進(jìn)行劃分,并推導(dǎo)各維修方式發(fā)生概率的基礎(chǔ)上建立了基于更新理論的設(shè)備長(zhǎng)期平均費(fèi)用率最優(yōu)模型,進(jìn)而求解設(shè)備的最優(yōu)檢測(cè)周期、預(yù)防性維修閾值。研究表明由于人因因素的影響,設(shè)備的平均費(fèi)用率增加,且隨著人因失誤概率的增加,設(shè)備的檢測(cè)周期和預(yù)防性維修的閾值均有減少。
[Abstract]:The optimal maintenance of equipment has been one of the problems that enterprises have been paying close attention to for a long time. With the rapid development of science and technology, although the safety and reliability of the equipment have been greatly improved, the maintenance cost of the equipment has not been significantly reduced. It is found that human error in maintenance is one of the important reasons for the increase of cost-cost rate. Especially, the quantity, variety, scale and complexity of the equipment in the enterprise are increasing, which makes it necessary for the equipment maintainers to improve their professional knowledge and skills. In practice, the gap between the professional knowledge and skills of maintenance personnel and the renewal rate of equipment is large, which leads to the increasing error rate of maintenance personnel, which indicates that the influence of human factors on equipment condition detection and maintenance effect is becoming more and more obvious. Therefore, considering the influence of human factors on condition detection, a more reasonable maintenance scheme can be worked out. In this paper, the "human factor" is introduced into the process of equipment maintenance decision optimization, and its modeling and optimization are studied. The main contents are as follows: first, using the functional resonance model, taking the condition based maintenance as the research object, It is divided into five functional units, the functional resonance network is established, and the possible human factors in the process are analyzed. It is found that human error mainly occurs in data collection and diagnosis and detection from data collection to decision making. Then, the human error factors identified in the condition based maintenance are quantitatively analyzed. Through the method of combining AHP with successful likelihood method, the probability of fault in diagnosis and detection is calculated and the causes are analyzed because of the lack of relevant personnel's experience, professional skills and knowledge. Finally, taking the single equipment system as the research object, the optimal maintenance decision modeling and optimization research are carried out considering the human factors. On the basis of dividing the deterioration state of equipment and deducing the probability of each maintenance mode, an optimal model of equipment long-term average cost rate based on renewal theory is established, and then the optimal detection period and preventive maintenance threshold of equipment are solved. The results show that the average cost rate of the equipment increases due to human factors, and the detection period and the threshold value of preventive maintenance decrease with the increase of the probability of human error.
【學(xué)位授予單位】：太原科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F273.4

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