本文選題：盾構(gòu)機(jī)推進(jìn)系統(tǒng) + 故障預(yù)測(cè)��； 參考：《南京理工大學(xué)》2014年碩士論文

【摘要】：盾構(gòu)機(jī)作為一種被廣泛應(yīng)用于城市地鐵建設(shè)的大型工程機(jī)械,其工作條件受到多種自然環(huán)境的影響,容易發(fā)生故障,因此對(duì)其故障預(yù)測(cè)技術(shù)的研究有十分重要的意義,但是采用傳統(tǒng)故障預(yù)測(cè)技術(shù)很難滿足要求,而隨著人工智能故障預(yù)測(cè)技術(shù)的出現(xiàn)及其在實(shí)際工程應(yīng)用中取得了很好的預(yù)測(cè)效果,所以對(duì)盾構(gòu)機(jī)的故障采用智能預(yù)測(cè)方法變得現(xiàn)實(shí)可行。本文主要是通過(guò)對(duì)專家系統(tǒng)理論知識(shí)的分析,并結(jié)合模糊邏輯理論和神經(jīng)網(wǎng)絡(luò)知識(shí)的技術(shù)優(yōu)勢(shì),對(duì)盾構(gòu)機(jī)推進(jìn)系統(tǒng)的故障預(yù)測(cè)進(jìn)行了初步的探討,完成了如下幾個(gè)方面的工作： (1)建立了盾構(gòu)機(jī)推進(jìn)系統(tǒng)的故障知識(shí)庫(kù)。對(duì)盾構(gòu)機(jī)推進(jìn)系統(tǒng)的故障產(chǎn)生機(jī)理進(jìn)行了分析,將其故障分為了淺層故障知識(shí)和深層故障知識(shí),并對(duì)與盾構(gòu)機(jī)推進(jìn)系統(tǒng)相關(guān)的故障征兆參數(shù)進(jìn)行了選取,同時(shí)引入數(shù)據(jù)庫(kù)技術(shù)對(duì)故障知識(shí)庫(kù)進(jìn)行了設(shè)計(jì)和處理。 (2)研究了盾構(gòu)機(jī)推進(jìn)系統(tǒng)的故障預(yù)測(cè)推理機(jī)的算法。針對(duì)盾構(gòu)機(jī)推進(jìn)系統(tǒng)故障的復(fù)雜性和不確定性,引入模糊邏輯理論和神經(jīng)網(wǎng)絡(luò)知識(shí)對(duì)其故障預(yù)測(cè)推理機(jī)分別進(jìn)行設(shè)計(jì)與仿真,在對(duì)比分析了它們的優(yōu)缺點(diǎn)與精確度之后,提出將模糊神經(jīng)網(wǎng)絡(luò)運(yùn)用于故障預(yù)測(cè)推理機(jī)的設(shè)計(jì)之中,并在MATLAB軟件中對(duì)模糊神經(jīng)網(wǎng)絡(luò)故障預(yù)測(cè)算法進(jìn)行了實(shí)驗(yàn)仿真,其仿真結(jié)果具有更高的精度,證明了其在盾構(gòu)機(jī)推進(jìn)系統(tǒng)故障預(yù)測(cè)中的有效性和準(zhǔn)確性。 (3)設(shè)計(jì)了盾構(gòu)機(jī)推進(jìn)系統(tǒng)的故障預(yù)測(cè)軟件。結(jié)合軟件設(shè)計(jì)原則,本文選擇VisualC++6.0軟件作為專家系統(tǒng)的軟件設(shè)計(jì)平臺(tái),并通過(guò)OPC技術(shù)完成了VC與WinCC軟件的數(shù)據(jù)交換,采用COM組件技術(shù)實(shí)現(xiàn)了VC對(duì)MATLAB編寫的神經(jīng)網(wǎng)絡(luò)和模糊神經(jīng)網(wǎng)絡(luò)故障預(yù)測(cè)算法的調(diào)用,同時(shí)在開(kāi)發(fā)過(guò)程中采用界面化和模塊化設(shè)計(jì)方式,使得對(duì)系統(tǒng)軟件功能模塊的擴(kuò)充更加方便,也更加符合整個(gè)系統(tǒng)軟件的設(shè)計(jì)要求。
[Abstract]:As a kind of large-scale construction machinery widely used in urban subway construction, the working conditions of shield machine are affected by many kinds of natural environment and are prone to failure. Therefore, it is of great significance to study the fault prediction technology of shield machine. However, it is difficult to meet the requirements by using the traditional fault prediction technology, and with the emergence of artificial intelligence fault prediction technology and its application in practical engineering has achieved a very good prediction effect. So it is feasible to apply intelligent prediction method to shield machine fault. Based on the analysis of expert system theory knowledge and the technical advantages of fuzzy logic theory and neural network knowledge, this paper makes a preliminary discussion on the fault prediction of shield machine propulsion system, and accomplishes the following work: The fault knowledge base of shield machine propulsion system is established. The fault generation mechanism of shield machine propulsion system is analyzed, the fault is divided into shallow fault knowledge and deep fault knowledge, and the fault symptom parameters related to shield machine propulsion system are selected. At the same time, the database technology is introduced to design and deal with the fault knowledge base. The algorithm of fault prediction inference machine for shield machine propulsion system is studied. In view of the complexity and uncertainty of the fault of shield machine propulsion system, the fuzzy logic theory and neural network knowledge are introduced to design and simulate the fault prediction inference machine respectively. The fuzzy neural network is applied to the design of the fault prediction inference machine, and the simulation of the fuzzy neural network fault prediction algorithm is carried out in the MATLAB software. The simulation results show that the simulation results have higher accuracy. The validity and accuracy of this method in fault prediction of shield machine propulsion system are proved. The software of fault prediction for shield machine propulsion system is designed. Combined with the principle of software design, this paper chooses VisualC 6.0 software as the software design platform of expert system, and completes the data exchange between VC and WinCC software through OPC technology. The COM component technology is used to realize the call of the neural network and fuzzy neural network fault prediction algorithm written by MATLAB by VC. At the same time, the interface and modularization design method are adopted in the development process. It makes it more convenient to expand the function module of the system software and meets the design requirements of the whole system software.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U455.39

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