本文選題：故障診斷 + 健康管理　； 參考：《中國(guó)科學(xué)院大學(xué)(中國(guó)科學(xué)院沈陽(yáng)計(jì)算技術(shù)研究所)》2017年碩士論文

【摘要】：設(shè)備健康與故障分析,一般依托于PHM(Prognostics and Health Management故障診斷與健康管理)系統(tǒng)。在一般的PHM系統(tǒng)中,常常利用大數(shù)據(jù)技術(shù)與統(tǒng)計(jì)學(xué)習(xí)算法,對(duì)生產(chǎn)過程中產(chǎn)生的各類數(shù)據(jù)進(jìn)行分析,通過分析結(jié)論去量化與評(píng)估工業(yè)設(shè)備的健康狀態(tài),同時(shí)預(yù)測(cè)故障的發(fā)生過程與失效時(shí)間。PHM技術(shù)將設(shè)備的健康管理從傳統(tǒng)的故障管理轉(zhuǎn)變?yōu)樗ネ斯芾?通過預(yù)測(cè)性維護(hù)實(shí)現(xiàn)設(shè)備的零宕機(jī)和持續(xù)可靠的運(yùn)行。目前大部分PHM系統(tǒng)是底層傳感器與數(shù)據(jù)分析工具相關(guān)聯(lián)的架構(gòu)設(shè)計(jì),往往對(duì)于每一類設(shè)備都有專用的分析套件,具有很強(qiáng)的專用度,但是缺乏重構(gòu)性與算法通用性。另一方面,對(duì)于產(chǎn)生數(shù)據(jù)規(guī)模不大的設(shè)備均采用單機(jī)模式,由于設(shè)計(jì)模式限制,分析設(shè)備往往與監(jiān)測(cè)設(shè)備之間直接相連。對(duì)于大量設(shè)備產(chǎn)生的大規(guī)模數(shù)據(jù)分析任務(wù),在低響應(yīng)時(shí)間需求下,單臺(tái)分析設(shè)備的處理能力往往是不夠的�；谝陨闲枨�,本文提出了可重構(gòu),通用化的PHM系統(tǒng)流程,同時(shí)實(shí)現(xiàn)了分布式集群下的工業(yè)大數(shù)據(jù)分析平臺(tái),提高了系統(tǒng)的適用性與運(yùn)行效率。本文以工業(yè)設(shè)備的健康管理與故障診斷分析方法為主要研究?jī)?nèi)容,首先介紹了課題背景與PHM系統(tǒng)的發(fā)展現(xiàn)狀,分析了其設(shè)計(jì)架構(gòu)與平臺(tái)特性。隨后對(duì)論文中涉及到數(shù)據(jù)挖掘算法模型與Hadoop,Spark,Django等平臺(tái)技術(shù)做了詳細(xì)闡述。之后本文對(duì)典型的工業(yè)設(shè)備包括軸承與工業(yè)電容的預(yù)后算法進(jìn)行研究,了解了一般PHM系統(tǒng)的基本分析流程。接著對(duì)通用的PHM平臺(tái)進(jìn)行了頂層架構(gòu)設(shè)計(jì),其中包括數(shù)據(jù)獲取模式,算法平臺(tái)的六層架構(gòu)與評(píng)價(jià)測(cè)試方法等基本模塊。最后提出了基于分布式平臺(tái)的PHM系統(tǒng)實(shí)現(xiàn)方案,包括集群的搭建,運(yùn)維,調(diào)優(yōu),算法的分布式移植等。
[Abstract]:Equipment health and fault analysis generally rely on the PHP Prognostics and Health Management Fault diagnosis and Health Management system. In a general big data system, big data technology and statistical learning algorithm are often used to analyze all kinds of data produced in the production process, and to quantify and evaluate the health status of industrial equipment through the conclusion of the analysis. At the same time, the technology of predicting the process of failure and failure time. PHM changes the health management of equipment from traditional fault management to decline management, and realizes the zero downtime and continuous reliable operation of the equipment through predictive maintenance. At present, most PHM systems are related to the underlying sensors and data analysis tools. They often have a special analysis suite for each type of equipment, and have a strong degree of specificity, but lack of reconfiguration and generality of the algorithm. On the other hand, the single machine mode is used for the equipment with small scale of generating data. Because of the limitation of design mode, the analysis equipment is often directly connected with the monitoring equipment. For large scale data analysis tasks generated by a large number of devices, the processing capacity of a single analysis device is often insufficient under low response time requirements. Based on the above requirements, this paper proposes a reconfigurable and generalizable big data system flow, and implements the industrial big data analysis platform under distributed cluster, which improves the applicability and running efficiency of the system. In this paper, the health management and fault diagnosis methods of industrial equipment are taken as the main research contents. Firstly, the background of the subject and the development status of PHM system are introduced, and its design framework and platform characteristics are analyzed. Then the data mining algorithm model and the technology of Hadoop Sparkor Django platform are described in detail. Then the prognostic algorithm of typical industrial equipment including bearing and industrial capacitance is studied and the basic analysis flow of general PHM system is understood. Then the general PHM platform is designed at the top level, which includes the data acquisition mode, the six-layer architecture of the algorithm platform and the evaluation and testing method. Finally, the realization scheme of PHM system based on distributed platform is put forward, including cluster construction, operation and maintenance, optimization, distributed migration of algorithm, etc.
【學(xué)位授予單位】：中國(guó)科學(xué)院大學(xué)(中國(guó)科學(xué)院沈陽(yáng)計(jì)算技術(shù)研究所)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP311.13

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