【摘要】：近年來(lái)，隨著國(guó)家經(jīng)濟(jì)的突飛猛進(jìn)及對(duì)基礎(chǔ)設(shè)施的大力投資，使得重大技術(shù)裝備行業(yè)蓬勃發(fā)展，而起重機(jī)械在重大技術(shù)裝備行業(yè)中占有很大比重。由于起重機(jī)械大部分是重型設(shè)備，一旦發(fā)生事故，經(jīng)濟(jì)損失慘重，極易造成人員傷亡。對(duì)此國(guó)家對(duì)起重機(jī)的使用安全提出了很高要求，并把其列為國(guó)家特種設(shè)備。通過(guò)對(duì)起重機(jī)事故的調(diào)查發(fā)現(xiàn)，其主要原因是疲勞斷裂。為此，各國(guó)政府和科研機(jī)構(gòu)開(kāi)始對(duì)起重機(jī)械疲勞斷裂問(wèn)題進(jìn)行大量研究，可望在設(shè)備疲勞斷裂之前能夠報(bào)告給用戶，從而避免事故的發(fā)生。 本文通過(guò)對(duì)金屬疲勞斷裂相關(guān)理論知識(shí)進(jìn)行深入的研究，得出解決起重機(jī)疲勞斷裂的先決條件是編制出能模擬起重機(jī)金屬結(jié)構(gòu)真實(shí)使用情況，具有代表性的典型載荷——時(shí)間歷程，即載荷譜。由于載荷的隨機(jī)性和不確定性，導(dǎo)致無(wú)法將實(shí)測(cè)結(jié)果直接應(yīng)用于理論分析與工程實(shí)踐，而需構(gòu)建一種能本質(zhì)反映起重機(jī)金屬結(jié)構(gòu)在各種工況下所受載荷隨時(shí)間變化的當(dāng)量載荷譜。 本文以鑄造橋式起重機(jī)和通用橋式起重機(jī)為研究對(duì)象，通過(guò)現(xiàn)場(chǎng)調(diào)研，收集了部分起重機(jī)工作狀況的數(shù)據(jù)樣本。并首次使用基于統(tǒng)計(jì)學(xué)習(xí)理論的支持向量機(jī)非線性回歸理論，通過(guò)對(duì)收集的樣本數(shù)據(jù)進(jìn)行訓(xùn)練，建立了相應(yīng)類型橋式起重機(jī)工作循環(huán)次數(shù)與不同起升載荷之間的非線性映射關(guān)系，利用此映射關(guān)系，可實(shí)現(xiàn)對(duì)相應(yīng)類型或未知橋式起重機(jī)當(dāng)量載荷譜的預(yù)測(cè)。本文利用可視化程序設(shè)計(jì)語(yǔ)言VC++6.0編制了基于支持向量機(jī)起重機(jī)載荷譜獲取與預(yù)測(cè)的應(yīng)用軟件，將軟件應(yīng)用于工程實(shí)例，并用軟件預(yù)測(cè)結(jié)果與實(shí)際結(jié)果進(jìn)行比較，表明具有較高的吻合性和實(shí)用性。然后，將此方法與最小二乘法和神經(jīng)網(wǎng)絡(luò)兩種方法進(jìn)行比較，利用同一樣本數(shù)據(jù)進(jìn)行起重機(jī)載荷譜的獲取與預(yù)測(cè)，證明了支持向量機(jī)方法的優(yōu)越性。而且，本軟件操作簡(jiǎn)單明了，使得操作人員不需要具備支持向量機(jī)的相關(guān)知識(shí)，就可以使用本軟件。最重要的是本研究成果為后續(xù)起重機(jī)疲勞壽命預(yù)測(cè)軟件的開(kāi)發(fā)奠定了基礎(chǔ)。
[Abstract]:In recent years, with the rapid development of national economy and the great investment in infrastructure, the major technical equipment industry is booming, and the lifting machinery occupies a large proportion in the major technical equipment industry. Because the lifting machinery is mostly heavy equipment, once the accident occurs, the economic loss is heavy, and it is easy to cause casualties. This country put forward the very high request to the crane safe use, and listed it as the national special equipment. Through the investigation of crane accident, it is found that the main reason is fatigue fracture. Therefore, many governments and scientific research institutions have begun to study the fatigue fracture of lifting machinery, which can be reported to the users before the fatigue fracture of the equipment, thus avoiding the occurrence of accidents. In this paper, through the deep research on the theory of metal fatigue fracture, it is concluded that the precondition to solve the fatigue fracture of crane is to draw up a program to simulate the real use of crane metal structure. Typical load-time history, namely load spectrum. Due to the randomness and uncertainty of load, it is impossible to directly apply the measured results to theoretical analysis and engineering practice. It is necessary to construct an equivalent load spectrum which can essentially reflect the variation of load on crane metal structure with time under various working conditions. In this paper, casting bridge crane and general bridge crane are taken as research objects, and some data samples of crane working condition are collected through field investigation. The support vector machine (SVM) nonlinear regression theory based on statistical learning theory is used for the first time to train the collected sample data. The nonlinear mapping relationship between the working cycle number of the corresponding type bridge crane and different lifting loads is established. Using this mapping relationship, the equivalent load spectrum of the corresponding type or unknown bridge crane can be predicted. In this paper, the application software of load spectrum acquisition and prediction based on support vector machine crane is programmed by using visual programming language VC 6.0.The software is applied to engineering example, and the result of prediction is compared with the actual result. It shows that it has high consistency and practicability. Then, the method is compared with the least square method and the neural network method, and the same sample data is used to obtain and predict the load spectrum of the crane, which proves the superiority of the support vector machine (SVM) method. Moreover, the software is easy to operate, so that the operator can use the software without the knowledge of support vector machine. The most important is that the research results lay a foundation for the development of the following crane fatigue life prediction software.
【學(xué)位授予單位】：太原科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH21

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本文編號(hào)：2208011