本文選題：關(guān)聯(lián)規(guī)則 + 大數(shù)據(jù)�。� 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：動(dòng)車組作為完成鐵路高速運(yùn)輸生產(chǎn)任務(wù)最重要的移動(dòng)設(shè)備,是高新技術(shù)的集成體。與傳統(tǒng)機(jī)車車輛相比動(dòng)車組在車輛結(jié)構(gòu)上有很大的不同,而且其運(yùn)行速度是傳統(tǒng)機(jī)車車輛所不可及的。在其運(yùn)營(yíng)過(guò)程中,故障管理和檢修是高速鐵路系統(tǒng)綜合保障工程中的重要組成部分,是確保實(shí)現(xiàn)動(dòng)車組安全運(yùn)行,高效率使用的必要保障。在檢修過(guò)程中,修程修制又起著指導(dǎo)性、關(guān)鍵性的作用,而且合理完善的修程修制是保證高速動(dòng)車組快速、安全、舒適、高效運(yùn)行的基本前提。然而,對(duì)安全問(wèn)題的重視,無(wú)疑會(huì)造成動(dòng)車組復(fù)雜的維修流程,這對(duì)于提升效率自然會(huì)是一個(gè)極大的影響。要提高動(dòng)車組的維修效率,一方面是深入對(duì)動(dòng)車組構(gòu)造的理論研究;另一方面,在過(guò)去積累的大量動(dòng)車組數(shù)據(jù)中包含著尚未發(fā)掘的有價(jià)值的信息。而隨著大數(shù)據(jù)相關(guān)技術(shù)的成熟,這些數(shù)據(jù)的價(jià)值也日益凸顯。為了使這些數(shù)據(jù)得到很好的利用,要從海量的故障數(shù)據(jù)中獲取其中隱含的故障關(guān)聯(lián)信息,以達(dá)到較早發(fā)現(xiàn)故障的目的。維修的策略主要有3種:周期修,狀態(tài)修和事后修。其中周期修是目前最為主要的一種方式,將維修等級(jí)分成五級(jí),列車服役一定的時(shí)間或里程后就會(huì)進(jìn)行相應(yīng)的維修,更換一些對(duì)應(yīng)的部件。此方法中,維修周期是根據(jù)專家經(jīng)驗(yàn)確定的,為了保證安全所以有一定的余地。這樣雖然保證了安全,但是會(huì)陷入到過(guò)度修的情況中,即列車上某部件健康情況良好卻依然被更換,導(dǎo)致運(yùn)維成本提高。事后修則是另一種極端,即當(dāng)部件完全失效時(shí)再進(jìn)行更換,這顯然是不可取的方案。故而就提出了折中的狀態(tài)修方案,根據(jù)部件當(dāng)前的工作狀態(tài),判斷其損壞程度,在其將要損壞時(shí)進(jìn)行更換,從而既保證了運(yùn)輸安全,又降低成本的目的。目前在我國(guó)的鐵路事業(yè)中,大數(shù)據(jù)分析技術(shù)已經(jīng)運(yùn)用到了一些領(lǐng)域中:基于Hadoop平臺(tái)設(shè)計(jì)并實(shí)現(xiàn)了一種分析和處理動(dòng)車組振動(dòng)數(shù)據(jù)的方案,用于消除高鐵振動(dòng)數(shù)據(jù)中的線性漂移,發(fā)現(xiàn)數(shù)據(jù)中的異常點(diǎn),通過(guò)數(shù)據(jù)分布情況判斷列車部件故障的類型�；贖adoop平臺(tái),通過(guò)分析歷史車流數(shù)據(jù)來(lái)高效準(zhǔn)確的推算車流;提出了一種構(gòu)建動(dòng)車組數(shù)據(jù)倉(cāng)庫(kù)的思路。其中也包括動(dòng)車組故障數(shù)據(jù)的相關(guān)部分,可以說(shuō)大數(shù)據(jù)分析對(duì)于龐大的鐵路系統(tǒng)來(lái)說(shuō)是未來(lái)的發(fā)展方向,并且也已經(jīng)在動(dòng)車組的運(yùn)營(yíng)管理的某些領(lǐng)域中得到了應(yīng)用。隨著動(dòng)車組維修領(lǐng)域的需求日益增長(zhǎng),動(dòng)車組故障檢修方面也必將需要大數(shù)據(jù)分析技術(shù)的支持。大數(shù)據(jù)數(shù)據(jù)挖掘過(guò)程一般由數(shù)據(jù)清洗、數(shù)據(jù)集成、數(shù)據(jù)轉(zhuǎn)換、數(shù)據(jù)挖掘、模式評(píng)估和知識(shí)表示這幾個(gè)階段組成。在具體挖掘過(guò)程中,需要這幾個(gè)階段的反復(fù)執(zhí)行。數(shù)據(jù)挖掘主要分為關(guān)聯(lián)模式挖掘,聚類模式挖掘,決策樹模式挖掘等;而本文的主要工作:關(guān)聯(lián)規(guī)則挖掘,主要分為挖掘頻繁模式和根據(jù)頻繁模式生成關(guān)聯(lián)規(guī)則兩步。其中關(guān)聯(lián)規(guī)則的生成較為簡(jiǎn)單,所以影響關(guān)聯(lián)規(guī)則算法效率的主要步驟是頻繁模式的挖掘,也是區(qū)分諸多算法效率的核心問(wèn)題。因此在頻繁模式挖掘方面取得的任何進(jìn)展都將對(duì)關(guān)聯(lián)規(guī)則以至于其他的數(shù)據(jù)挖掘任務(wù)的效率產(chǎn)生重要影響。綜上所述,本文通過(guò)在分布式計(jì)算平臺(tái)上實(shí)現(xiàn)關(guān)聯(lián)關(guān)系規(guī)則算法,用于分析動(dòng)車組故障數(shù)據(jù)。填補(bǔ)我國(guó)目前動(dòng)車組運(yùn)維方面的不足。最早的關(guān)聯(lián)規(guī)則算法可以追溯到1993年,名叫AIS算法。但由于該算法效率過(guò)低,在由Agrwal等人的改進(jìn)后提出了 Apriori算法,特點(diǎn)是使用了逐層搜索的迭代思路來(lái)找出事務(wù)數(shù)據(jù)庫(kù)中的頻繁項(xiàng)集,相較于AIS其效率大大的提高。作為一種經(jīng)典算法,后來(lái)的許多算法比如AprioriHybrid等算法皆是依據(jù)它改進(jìn)而來(lái)的。Apriori算法主要通過(guò)兩個(gè)頻繁項(xiàng)集的重要特性,使得整個(gè)算法的效率提升:如項(xiàng)目集R是頻繁項(xiàng)集,則其子集也是頻繁項(xiàng)集;如R不是頻繁項(xiàng)集,則其超集都是非頻繁項(xiàng)集。通過(guò)這兩個(gè)性質(zhì),可以有效的減少頻繁項(xiàng)集的產(chǎn)生。Apriori算法使用的是一種迭代方法,叫做逐層搜索,其中k項(xiàng)集用于探索(k+1)項(xiàng)集。首先,掃描數(shù)據(jù)庫(kù),累積每個(gè)單獨(dú)項(xiàng)的計(jì)數(shù),并記錄每個(gè)滿足最小支持度的項(xiàng),即找出頻繁1項(xiàng)集的集合,記為L(zhǎng)1。然后根據(jù)這個(gè)找出L2,即頻繁2項(xiàng)集的集合。以此類推,只到不能再找到頻繁k項(xiàng)集。一次數(shù)據(jù)庫(kù)的完整掃描只能完成一次找出Lk的操作。除了在故障診斷方面Apriori算法能發(fā)揮巨大的作用之外,該算法在商業(yè),價(jià)格分析等領(lǐng)域中都得到了廣泛的應(yīng)用。該算法具有直觀,簡(jiǎn)便易于實(shí)現(xiàn)等特點(diǎn),同樣也有候選項(xiàng)集多,數(shù)據(jù)庫(kù)掃描次數(shù)多等方面的不足。可以說(shuō)是優(yōu)點(diǎn)與缺點(diǎn)同樣明顯。本文根據(jù)算法的缺點(diǎn)進(jìn)行了改進(jìn),考慮從蟻群優(yōu)化和布隆過(guò)濾器兩種思路對(duì)算法的性能做出優(yōu)化,主要是在產(chǎn)生關(guān)聯(lián)關(guān)系的中間過(guò)程中消除一些冗余,使得算法能更加快速的執(zhí)行。并對(duì)比算法之間的性能,選取性能更優(yōu)的算法用于進(jìn)一步工作;另一方面,為了更好的分析數(shù)據(jù),就要使用大數(shù)據(jù)工具,才能高效,合理的進(jìn)行計(jì)算。本文對(duì)于大數(shù)據(jù)平臺(tái)Hadoop進(jìn)行深入研究,包括分布式文件系統(tǒng)(Hadoop Distributed File System)以及 Spark 框架。HDFS作為主流的分布式存儲(chǔ)系統(tǒng),主要有以下優(yōu)點(diǎn):①擴(kuò)容能力:能更可靠的存儲(chǔ)和處理PB級(jí)的數(shù)據(jù);②成本低:可以通過(guò)普通機(jī)器組成的服務(wù)群來(lái)分發(fā)以及處理數(shù)據(jù),這些服務(wù)器總計(jì)可達(dá)數(shù)千個(gè)節(jié)點(diǎn)。③高效率:通過(guò)分發(fā)數(shù)據(jù)和備份數(shù)據(jù),Hadoop可以在數(shù)據(jù)所在的節(jié)點(diǎn)上并行的處理他們。④高容錯(cuò)性:在面對(duì)數(shù)據(jù)可能損害或出錯(cuò)時(shí),不是采用使用更好的機(jī)器以防止出錯(cuò)這種策略,而是提供了一種機(jī)制,使得普通機(jī)器節(jié)點(diǎn)上的數(shù)據(jù)損壞出錯(cuò)后也能很好的處理。可以說(shuō),HDFS是面向一種數(shù)據(jù)高出錯(cuò)率的一種解決方案。這種容錯(cuò)性高的特點(diǎn)可以保證數(shù)據(jù)安全可靠更可以使其可以部署在一般的普通商業(yè)機(jī)器上。Spark是一個(gè)基于內(nèi)存計(jì)算的開源的集群計(jì)算系統(tǒng),目的是讓數(shù)據(jù)分析更加快速。Spark非常小巧玲瓏,由加州伯克利大學(xué)AMP實(shí)驗(yàn)室的Matei為主的小團(tuán)隊(duì)所開發(fā)。Spark是一種與Hadoop相似的開源集群計(jì)算環(huán)境,但是兩者之間還存在一些不同之處,這些有用的不同之處使Spark在某些工作負(fù)載方面表現(xiàn)得更加優(yōu)越,換句話說(shuō),Spark啟用了內(nèi)存分布數(shù)據(jù)集,除了能夠提供交互式查詢外,它還可以優(yōu)化迭代工作負(fù)載。Spark是在Scala語(yǔ)言中實(shí)現(xiàn)的,它將Scala用作其應(yīng)用程序框架。與Hadoop不同,Spark和Scala能夠緊密集成,其中的Scala可以像操作本地集合對(duì)象一樣輕松地操作分布式數(shù)據(jù)集。盡管創(chuàng)建Spark是為了支持分布式數(shù)據(jù)集上的迭代作業(yè),但是實(shí)際上它是對(duì)Hadoop的補(bǔ)充,可以在Hadoop文件系統(tǒng)中并行運(yùn)行。最后,以關(guān)聯(lián)規(guī)則算法和大數(shù)據(jù)平臺(tái)為基礎(chǔ),將前期理論知識(shí)和動(dòng)車組故障數(shù)據(jù)相結(jié)合,確定故障關(guān)聯(lián)規(guī)則的挖掘方案。最終達(dá)到高速準(zhǔn)確的挖掘動(dòng)車組故障關(guān)聯(lián)規(guī)則的目的,為管理部門制定更加完善,合理的動(dòng)車組維修流程提供優(yōu)化建議。隨著動(dòng)車組的大規(guī)模應(yīng)用,維修管理規(guī)程得到了補(bǔ)充,修訂和完善。使得檢修計(jì)劃和作業(yè)流程得到調(diào)整優(yōu)化,但由于尚在起步階段,檢修計(jì)劃會(huì)隨著鐵路建設(shè),部件壽命等變動(dòng)而調(diào)整。所以,很多方面我國(guó)仍處于研究階段。我國(guó)大數(shù)據(jù)分析主要面對(duì)的問(wèn)題是投入產(chǎn)出比不高,消耗的資源較高但是沒有產(chǎn)生應(yīng)有的效應(yīng)。但從長(zhǎng)遠(yuǎn)來(lái)看,隨著相關(guān)行業(yè)的規(guī)范化和各行業(yè)原始數(shù)據(jù)的積累,大數(shù)據(jù)分析的前景必定廣闊。本論文"基于大數(shù)據(jù)的動(dòng)車組故障關(guān)聯(lián)關(guān)系規(guī)則挖掘算法研究與實(shí)現(xiàn)"是基于動(dòng)車組運(yùn)維數(shù)據(jù)來(lái)實(shí)現(xiàn)動(dòng)車組故障知識(shí)的獲取,優(yōu)化等工作。本研究實(shí)現(xiàn)了從海量動(dòng)車組故障數(shù)據(jù)中利用改進(jìn)的Apriori算法挖掘出故障的頻繁項(xiàng)集和關(guān)聯(lián)規(guī)則,并根據(jù)算法的不足進(jìn)行改進(jìn);以及將改進(jìn)后算法移植到Spark下更快速的完成上述工作。
[Abstract]:As the most important mobile equipment to complete the high speed transportation production task of railway, the EMU is the integration of high and new technology. Compared with the traditional locomotive, the EMU has a great difference in the vehicle structure, and its running speed is not available by the traditional locomotive. In its operation course, the fault management and maintenance are the high-speed railway system. The important part of the comprehensive guarantee project is the necessary guarantee to ensure the safe operation and efficient use of the EMU. During the maintenance process, the repair system plays a guiding and key role, and a reasonable and perfect repair system is the basic premise to ensure the rapid, safe, comfortable and efficient operation of the high speed EMU. The attention of the whole problem will undoubtedly cause the complex maintenance process of the EMU, which will naturally have a great influence on the efficiency of the lifting. To improve the maintenance efficiency of the EMU, it is the theoretical study of the EMU structure on the one hand; on the other hand, the large number of EMU data that has accumulated in the past contains the value that has not been excavated. With the maturity of large data related technologies, the value of these data is becoming more and more prominent. In order to make good use of these data, it is necessary to obtain the hidden fault association information from the massive failure data to achieve the purpose of early detection. There are 3 main maintenance strategies: periodic repair, state repair and post repair. The middle period repair is the most important way at present. The maintenance grade is divided into five levels. The train will be repaired after a certain time or mileage, and the corresponding parts will be replaced. In this method, the maintenance cycle is determined according to the experience of the expert, in order to ensure the safety and safety, this ensures the safety, But in the case of excessive repair, that is, a part of the train is in good health and is still being replaced, resulting in an increase in the cost of operation and maintenance. The latter is another extreme, that is, the replacement of the component when the component is completely invalid. This is obviously an undesirable scheme. Therefore, a compromise state repair scheme is proposed, based on the current work of the component. State, to judge the extent of its damage and replace it when it will be damaged, which not only ensures the safety of transportation, but also reduces the cost. At present, the large data analysis technology has been used in some fields in our country's railway industry. Based on the Hadoop platform, a scheme for analyzing and dealing with the vibration data of the EMU is designed and implemented. In order to eliminate the linear drift in the high speed rail vibration data, the anomaly points in the data are found and the type of the train component fault is judged by the data distribution. Based on the Hadoop platform, the data of the historical traffic flow is used to calculate the traffic flow efficiently and accurately. A train of thought for the construction of the EMU data warehouse is proposed. According to the related parts, it can be said that large data analysis is the future development direction for the large railway system, and has been applied in some areas of the operation management of the EMU. With the increasing demand of the EMU maintenance field, the fault maintenance of EMU will also need the support of large data analysis technology. The process of data mining in large data is usually composed of data cleaning, data integration, data conversion, data mining, pattern evaluation and knowledge representation. In the concrete mining process, the repeated execution of these stages is needed. Data mining is mainly divided into association pattern mining, clustering pattern mining, decision tree pattern mining, and so on. The main work: mining association rules is divided into two steps: mining frequent patterns and generating association rules according to frequent patterns. The generation of association rules is relatively simple, so the main steps that affect the efficiency of association rules algorithm are the mining of frequent patterns, and also the core problem to distinguish the efficiency of many algorithms. Any progress made will have an important impact on the efficiency of association rules and other data mining tasks. To sum up, this paper implements the algorithm of association rules on the distributed computing platform to analyze the malfunction data of the EMU. The algorithm can be traced back to 1993, called AIS algorithm. But because of the low efficiency of the algorithm, the Apriori algorithm is proposed after the improvement of Agrwal et al. The characteristic is to use the iterative idea of layer by layer search to find frequent itemsets in the transaction database, which is greatly improved compared to the efficiency of AIS. As a classic algorithm, many later calculations are made. The algorithm, such as AprioriHybrid, is based on its improved.Apriori algorithm, mainly through the important properties of two frequent itemsets, to improve the efficiency of the whole algorithm: if the item set R is a frequent itemset, then its subset is also a frequent itemset; for example, R is not a frequent itemset, and its superset is infrequent itemsets. Through these two properties, To effectively reduce frequent itemsets generation.Apriori algorithm is an iterative method called an iterative method called layer by layer, where k sets are used to explore (k+1) sets. First, the database is scanned, the count of each individual item is accumulated, and each item that satisfies the minimum support is recorded, that is, to find a set of frequent 1 sets, recorded as L1. and then based on this search. L2, that is, the set of frequent 2 sets. By analogy, only the frequent K itemsets can not be found. A complete scan of the database can only be completed to find the operation of Lk once. Besides the great role of the Apriori algorithm in fault diagnosis, the algorithm has been widely used in the fields of business, price analysis and so on. It has the characteristics of intuitionistic, simple and easy to implement. There are also many candidate items and many shortcomings of database scanning. It can be said that the advantages and disadvantages are equally obvious. In this paper, the shortcomings of the algorithm are improved and the performance of the algorithm is optimized from two ideas of ant colony optimization and blon filter. In the middle process of the connection, some redundancy can be eliminated so that the algorithm can be executed more quickly. And the performance of the algorithm is compared with the algorithm of better performance. On the other hand, in order to better analyze the data, it is necessary to use large data tools to achieve high efficiency and reasonable calculation. In this paper, the large data platform Hadoo P's in-depth study, including the Hadoop Distributed File System and the Spark framework.HDFS as the mainstream distributed storage system, has the following advantages: (1) capacity expansion: more reliable storage and processing of PB level data; and low cost: can be distributed and processed by a service group composed of ordinary machines. Data, the total number of these servers can reach thousands of nodes. 3. High efficiency: by distributing data and backing up the data, Hadoop can handle them parallel to the nodes of the data. 4. High fault tolerance: instead of using a better machine to prevent the error in the face of data damage or error, it provides a machine. As a result, HDFS is a solution to a high error rate of data. The high fault tolerance can ensure that the data is safe and reliable and can be deployed on the ordinary common business machine and.Spark is a memory based calculation. The open source cluster computing system is designed to make data analysis more rapid and.Spark very small. The.Spark is an open source cluster computing environment similar to Hadoop, developed by a small team based on Matei of the AMP laboratory in Berkeley University of California. But there are some differences between the two, and these useful differences make Spark In some of the workload performance, in other words, Spark enabled the memory distribution dataset, in addition to providing interactive queries, it also optimizes the iterative workload.Spark to be implemented in the Scala language, which uses Scala as its application framework. Unlike Hadoop, Spark and Scala can be tightly integrated, Scala in a distributed data set can be manipulated as easily as the local collection object. Although the creation of Spark is to support iterative jobs on a distributed data set, it is actually a supplement to the Hadoop and can run in parallel in the Hadoop file system. Finally, the former is based on the custom rule algorithm and the large data platform. With the combination of the theoretical knowledge and the malfunction data of the EMU, the mining scheme of the fault association rules is determined. Finally, the purpose of high speed and accurate mining of the mus fault association rules is achieved, and the optimization proposal for the management department to make a more perfect and reasonable EMU maintenance process is provided. With the large-scale application of the EMU, the maintenance management rules are obtained. It has been supplemented, revised and perfected. The maintenance plan and operation process have been adjusted and optimized. But because of the initial stage, the maintenance plan will be adjusted with the railway construction and the changes in the component life. So, in many aspects, our country is still in the stage of research. But in the long run, with the standardization of the related industries and the accumulation of the original data in various industries, the prospect of the large data analysis must be broad. This paper "research and implementation of the algorithm for mining fault association rules based on large data" is based on EMU Operation and maintenance data to realize the movement. In this study, we use improved Apriori algorithm to excavate frequent item sets and association rules from the malfunction data of mass EMU, and improve the algorithm according to the shortcomings of the algorithm. And the improved algorithm is transplanted to Spark to complete the work more quickly.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U269;TP311.13

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