本文選題：云計算 + 設(shè)備狀態(tài)維護��； 參考：《西安科技大學》2017年碩士論文

【摘要】：設(shè)備維護作為提升現(xiàn)代化企業(yè)核心競爭力的重要因素,是企業(yè)生產(chǎn)效益和經(jīng)濟效益的重要保證,如何制定設(shè)備的維修計劃以及采取何種維修行動是企業(yè)進行設(shè)備維護需要解決的關(guān)鍵性問題。設(shè)備運行低故障甚至零故障的期望,使得企業(yè)更加注重運用先進的檢測、診斷技術(shù)保證設(shè)備的健康運行,與此同時也產(chǎn)生了大量的設(shè)備運行、故障等方面的信息。針對上述問題,本文將云計算技術(shù)和設(shè)備維護技術(shù)相結(jié)合,提出了基于Hadoop的設(shè)備狀態(tài)維護云平臺,為現(xiàn)代企業(yè)進行設(shè)備維護提供了一種新方法和新思路。首先,基于Hadoop技術(shù),建立設(shè)備狀態(tài)維護云平臺的體系架構(gòu),對網(wǎng)絡(luò)各層進行布局,并進一步設(shè)計設(shè)備狀態(tài)維護服務(wù)層在設(shè)備維修管理方面的功能模塊,根據(jù)各功能模塊的作用對其進行詳細論述。其次,研究以期望平均維護總費用最小為目標的設(shè)備狀態(tài)維護模型,綜合分析考慮設(shè)備失效率、最小修理費用和停機造成的損失等因素對模型的影響,在此基礎(chǔ)上,提出一種基于遺傳算法的決策模型優(yōu)化求解方法,并進行實例驗證。最后,為了提高和優(yōu)化計算速度,研究基于MapReduce計算模型和遺傳算法相結(jié)合的快速求解方法,提高求解效率,并開發(fā)程序和驗證。通過對基于云計算的設(shè)備狀態(tài)維護關(guān)鍵技術(shù)研究,可以有效的為設(shè)備維護行動提供決策建議,并且具有求解速度快,資源利用合理等優(yōu)勢,很好的滿足了企業(yè)的需要也促進了設(shè)備維護技術(shù)的進步。
[Abstract]:As an important factor to enhance the core competitiveness of modern enterprises, the maintenance of equipment is an important guarantee for the production and economic benefits of enterprises. How to make maintenance plan and how to take maintenance action are the key problems to be solved. The expectation of low fault or zero fault makes the enterprise pay more attention to the application of advanced detection and diagnosis technology to ensure the healthy operation of the equipment. At the same time it also produces a lot of equipment operation fault and other aspects of information. Aiming at the above problems, this paper combines cloud computing technology with equipment maintenance technology, and puts forward a state maintenance cloud platform based on Hadoop, which provides a new method and a new idea for modern enterprises to carry out equipment maintenance. First of all, based on the Hadoop technology, the system architecture of the equipment state maintenance cloud platform is established, and the layout of each layer of the network is carried out, and the function module of the equipment state maintenance service layer in the equipment maintenance management is further designed. According to the function of each functional module, it is discussed in detail. Secondly, the paper studies the equipment state maintenance model, which aims at the minimum average total maintenance cost, and comprehensively analyzes the effects of the failure rate, the minimum repair cost and the loss caused by downtime on the model. A decision model optimization method based on genetic algorithm is proposed and verified by an example. Finally, in order to improve and optimize the calculation speed, a fast solution method based on the combination of MapReduce computing model and genetic algorithm is studied to improve the efficiency of the solution, and develop the program and verify it. Through the research on the key technology of equipment state maintenance based on cloud computing, it can effectively provide decision advice for equipment maintenance, and has the advantages of fast solution, reasonable utilization of resources and so on. Very good to meet the needs of enterprises and promote the progress of equipment maintenance technology.
【學位授予單位】：西安科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP3;TP18

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本文編號：1946347