本文選題：故障診斷 + 推土機(jī)　； 參考：《山東大學(xué)》2017年碩士論文

【摘要】：混合動(dòng)力推土機(jī)是混合動(dòng)力技術(shù)在工程機(jī)械領(lǐng)域的創(chuàng)新應(yīng)用,混合動(dòng)力推土機(jī)由于系統(tǒng)多、結(jié)構(gòu)復(fù)雜、運(yùn)行工況惡劣,其故障的產(chǎn)生原因較為復(fù)雜,因此使用傳統(tǒng)建模的方法進(jìn)行故障診斷較為困難。近年來智能理論的應(yīng)用發(fā)展十分迅速,以歷史數(shù)據(jù)為基礎(chǔ)的故障診斷方法也在故障診斷領(lǐng)域中發(fā)揮了十分重要的作用,本文就是基于此趨勢進(jìn)行混合動(dòng)力推土機(jī)的故障診斷研究�；谝�(guī)則的推理技術(shù)(RBR,Rule-based Reasoning)在故障診斷方面有著重要應(yīng)用,它對前人經(jīng)驗(yàn)進(jìn)行系統(tǒng)化總結(jié)并模擬專家解決疑問的思維方式對當(dāng)前問題進(jìn)行求解�；趯�(shí)例的推理技術(shù)(CBR,Case-based Reasoning)同樣在故障診斷方面應(yīng)用較廣,它基于通過歷史、監(jiān)控和實(shí)驗(yàn)采集到的實(shí)際案例對當(dāng)前問題進(jìn)行求解。本文通過遠(yuǎn)程監(jiān)控系統(tǒng)收集到車輛各系統(tǒng)數(shù)據(jù)后形成故障診斷數(shù)據(jù)庫并以此為基礎(chǔ)將RBR和CBR進(jìn)行集成,最終建立基于Hybrid CBR的混合動(dòng)力推土機(jī)故障診斷專家系統(tǒng)。此系統(tǒng)可以迅速找到故障發(fā)生的位置、發(fā)生的原因以及處理的方法,幫助操作人員對問題及時(shí)處理,對提高設(shè)備可靠性、縮短維修周期、降低維修成本有著十分重要的研究意義和應(yīng)用價(jià)值。基于規(guī)則的推理技術(shù)包括規(guī)則總結(jié)、規(guī)則推理、規(guī)則管理三大部分,其中規(guī)則總結(jié)是RBR中最為關(guān)鍵的技術(shù),也是RBR技術(shù)研究的關(guān)鍵點(diǎn)。而規(guī)則總結(jié)的目標(biāo)就是將領(lǐng)域內(nèi)的知識(shí)進(jìn)行總結(jié)形成可以用于故障診斷的規(guī)則庫。本文使用大數(shù)據(jù)關(guān)聯(lián)規(guī)則分析中的Apriori算法對通過遠(yuǎn)程監(jiān)控系統(tǒng)采集到的數(shù)據(jù)進(jìn)行挖掘,尋找出各特征項(xiàng)與故障之間的聯(lián)系,形成基于規(guī)則推理的故障診斷規(guī)則庫。并對利用此規(guī)則庫進(jìn)行RBR診斷進(jìn)行了詳細(xì)介紹�；趯�(shí)例的推理技術(shù)中實(shí)例檢索是最為關(guān)鍵的技術(shù),其檢索算法的使用直接關(guān)系到CBR檢索模塊和整個(gè)系統(tǒng)的效率。本文使用K最臨近(K-NN)算法進(jìn)行實(shí)例檢索,在此基礎(chǔ)上使用AHP層次分析法對特征項(xiàng)進(jìn)行加權(quán),使其能夠突出重點(diǎn),更精確的進(jìn)行檢索。系統(tǒng)的CBR模塊將檢索分為兩步,首先使用數(shù)據(jù)庫的SQL語言進(jìn)行初步檢索,再使用K-NN算法進(jìn)行精確檢索,從而在保證檢索精確度的同時(shí)提高了系統(tǒng)效率。本文將RBR和CBR兩種方式有機(jī)的串聯(lián)起來,構(gòu)建了基于Hybrid CBR的混合動(dòng)力推土機(jī)故障診斷專家系統(tǒng)。本系統(tǒng)使用Visual Studio 2010作為軟件開發(fā)環(huán)境,使用SQL Server 2008作為數(shù)據(jù)庫管理工具,并在系統(tǒng)不同模塊的開發(fā)中用到C#、R以及SQL語言作為編程語言。
[Abstract]:Hybrid power bulldozer is an innovative application of hybrid power technology in the field of engineering machinery. Due to many systems, complex structure and bad operating conditions, hybrid bulldozer is complicated, so it is difficult to use traditional modeling method to diagnose fault. In recent years, the application of intelligent theory is very rapid. The fault diagnosis method based on historical data also plays a very important role in the field of fault diagnosis. This paper is based on this trend to study the fault diagnosis of hybrid bulldozer. RBR (Rule-based Reasoning) has an important application in fault diagnosis, and it has been applied to the previous experience. CBR (Case-based Reasoning) is also widely used in fault diagnosis. It is based on the actual cases collected through history, monitoring and experiment. This paper is collected through remote monitoring system. After collecting the data of the vehicle system, the fault diagnosis database is formed and the RBR and CBR are integrated on this basis. Finally, the fault diagnosis expert system of hybrid power bulldozer based on Hybrid CBR is established. The system can quickly find the location of the fault, the cause and the method of the reason, and help the operator to get the problem in time. It has very important research significance and application value for improving equipment reliability, shortening maintenance cycle and reducing maintenance cost. Rule based reasoning technology includes rule summary, rule reasoning, rule management three parts. Rule summary is the most critical technology in RBR, and it is also the key point of RBR technology research. The goal is to sum up the knowledge in the field to form a rule base which can be used for fault diagnosis. In this paper, the Apriori algorithm in the analysis of large data association rules is used to excavate the data collected through the remote monitoring system and find out the connection between the features and the fault, and form a rule based fault diagnosis rule. RBR diagnosis is introduced in detail. Case retrieval is the most critical technology in case based reasoning. The use of the retrieval algorithm is directly related to the efficiency of the CBR retrieval module and the whole system. In this paper, the K nearest (K-NN) algorithm is used for instance retrieval, and on this basis, the AHP hierarchy is used. The CBR module of the system is divided into two steps. First, the system uses the SQL language of the database to carry out the preliminary retrieval, and then uses the K-NN algorithm for accurate retrieval, thus improving the system efficiency while guaranteeing the accuracy of the retrieval. This paper puts the RBR and CBR two kinds of parties in this paper. The Hybrid CBR based fault diagnosis expert system of hybrid power bulldozer based on Hybrid CBR is set up in series. The system uses Visual Studio as the software development environment, uses SQL Server as the database management tool, and uses C, R and SQL language as the programming language in the development of different modules of the system.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TU623.5

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1 王凱;基于Hybrid CBR的混合動(dòng)力推土機(jī)故障診斷專家系統(tǒng)研究[D];山東大學(xué);2017年

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本文編號(hào)：1924199