發(fā)布時間：2018-08-12 20:26

【摘要】：隨著經(jīng)濟的迅速發(fā)展和城市建設(shè)規(guī)模的擴大化、快速化,塔式起重機的重要作用日益突出,且機群協(xié)同作業(yè)的情況越來越普遍。與此同時,塔機事故隨著機群規(guī)模的擴大而頻繁發(fā)生,嚴重者甚至造成人員的傷亡。因此,為使整個機群有效運行、協(xié)調(diào)工作、減少因事故造成的經(jīng)濟損失和人員傷亡,及時、準確的檢測診斷出塔機的故障將具有重大的經(jīng)濟效益和學術(shù)研究價值。 首先,塔式起重機機群擁有巨大的工作數(shù)據(jù),如何利用這些數(shù)據(jù)來反映塔機的工作狀態(tài)并使相關(guān)人員及時掌握情況是故障檢測與診斷的關(guān)鍵。本文通過研究塔式起重機組成機構(gòu)的工作及檢測原理,總結(jié)出塔式起重機機群故障特點,為機群故障檢測與診斷方法的研究和系統(tǒng)設(shè)計奠定了基礎(chǔ)。 其次,研究了基于神經(jīng)網(wǎng)絡(luò)專家系統(tǒng)的塔式起重機故障檢測與診斷方法。該方法融合了神經(jīng)網(wǎng)絡(luò)的學習能力、自適應(yīng)能力、并行推理能力及聯(lián)想記憶能力和專家系統(tǒng)知識經(jīng)驗的豐富性和權(quán)威性優(yōu)勢,確保了故障檢測診斷的準確、快速性。文中以機群中最重要的四種工況為實例對神經(jīng)網(wǎng)絡(luò)專家系統(tǒng)在故障檢測與診斷中的應(yīng)用進行了驗證。 最后,設(shè)計了基于塔機監(jiān)控儀和監(jiān)控中心計算機通過GPRS網(wǎng)絡(luò)構(gòu)成的塔機機群故障檢測與診斷系統(tǒng)。論文介紹了塔機監(jiān)控儀的硬件部分和軟件部分的設(shè)計原理,監(jiān)控儀以單片機為核心,通過GPRS方式實現(xiàn)遠程數(shù)據(jù)通信。硬件采用了模塊化結(jié)構(gòu)設(shè)計,包括輸入模塊、輸出模塊、報警模塊、液晶顯示模塊和數(shù)據(jù)存儲模塊。在監(jiān)控中心軟件部分,采用MATLAB編制了故障檢測與診斷程序,采用Visual Basic設(shè)計了數(shù)據(jù)庫處理和人機界面,編制MATLAB與VB的接口、VB與SQL的接口程序,實現(xiàn)了機群故障檢測與診斷。 論文的創(chuàng)新之處在于,將神經(jīng)網(wǎng)絡(luò)專家系統(tǒng)應(yīng)用于塔機機群故障檢測與診斷中;塔機監(jiān)控儀采用三個單片機協(xié)同工作的設(shè)計方案,增強了系統(tǒng)的實時性和可靠性。
[Abstract]:With the rapid development of economy and the expansion of urban construction scale, the important role of tower crane is becoming more and more prominent, and the cooperative operation of cluster is becoming more and more common. At the same time, tower crane accidents occur frequently with the expansion of fleet size, and even cause casualties. Therefore, in order to make the whole cluster run effectively, coordinate the work, reduce the economic losses and casualties caused by the accident, timely and accurately detect and diagnose the tower crane fault will have great economic benefits and academic research value. First of all, the tower cranes have huge working data. How to use these data to reflect the working status of tower cranes and make relevant personnel know the situation in time is the key to fault detection and diagnosis. In this paper, the fault characteristics of tower crane cluster are summarized by studying the work and detection principle of the tower crane, which lays a foundation for the research of the fault detection and diagnosis method of tower crane and the design of the system. Secondly, the fault detection and diagnosis method of tower crane based on neural network expert system is studied. This method combines the learning ability, adaptive ability, parallel reasoning ability, associative memory ability and expert system knowledge and authoritative advantages of neural network, and ensures the accuracy and rapidity of fault detection and diagnosis. In this paper, the application of neural network expert system in fault detection and diagnosis is verified by taking the four most important working conditions in the cluster as examples. Finally, the fault detection and diagnosis system of tower crane group is designed based on tower crane monitor and monitor center computer through GPRS network. This paper introduces the design principle of the hardware and software parts of the tower crane monitoring instrument. The monitoring instrument takes the single chip computer as the core and realizes the remote data communication through GPRS mode. The hardware adopts modular structure design, including input module, output module, alarm module, LCD module and data storage module. In the software part of the monitoring center, the program of fault detection and diagnosis is programmed by MATLAB, the database processing and man-machine interface are designed by Visual Basic, and the interface program between VB and MATLAB and SQL is compiled, which realizes the fault detection and diagnosis of cluster. The innovation of this paper is that the neural network expert system is applied to the fault detection and diagnosis of tower crane cluster, and the tower crane monitor adopts the design scheme of three single chip computers to work together, which enhances the real-time and reliability of the system.
【學位授予單位】：沈陽理工大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH213.3;TP277

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本文編號：2180271