基于支持向量機起重機載荷譜獲取方法的研究
發(fā)布時間：2018-08-27 17:58
【摘要】：近年來，隨著國家經(jīng)濟的突飛猛進及對基礎設施的大力投資，使得重大技術裝備行業(yè)蓬勃發(fā)展，而起重機械在重大技術裝備行業(yè)中占有很大比重。由于起重機械大部分是重型設備，一旦發(fā)生事故，經(jīng)濟損失慘重，極易造成人員傷亡。對此國家對起重機的使用安全提出了很高要求，并把其列為國家特種設備。通過對起重機事故的調(diào)查發(fā)現(xiàn)，其主要原因是疲勞斷裂。為此，各國政府和科研機構開始對起重機械疲勞斷裂問題進行大量研究，可望在設備疲勞斷裂之前能夠報告給用戶，從而避免事故的發(fā)生。 本文通過對金屬疲勞斷裂相關理論知識進行深入的研究，得出解決起重機疲勞斷裂的先決條件是編制出能模擬起重機金屬結構真實使用情況，具有代表性的典型載荷——時間歷程，即載荷譜。由于載荷的隨機性和不確定性，導致無法將實測結果直接應用于理論分析與工程實踐，而需構建一種能本質(zhì)反映起重機金屬結構在各種工況下所受載荷隨時間變化的當量載荷譜。 本文以鑄造橋式起重機和通用橋式起重機為研究對象，通過現(xiàn)場調(diào)研，收集了部分起重機工作狀況的數(shù)據(jù)樣本。并首次使用基于統(tǒng)計學習理論的支持向量機非線性回歸理論，通過對收集的樣本數(shù)據(jù)進行訓練，建立了相應類型橋式起重機工作循環(huán)次數(shù)與不同起升載荷之間的非線性映射關系，利用此映射關系，可實現(xiàn)對相應類型或未知橋式起重機當量載荷譜的預測。本文利用可視化程序設計語言VC++6.0編制了基于支持向量機起重機載荷譜獲取與預測的應用軟件，將軟件應用于工程實例，并用軟件預測結果與實際結果進行比較，表明具有較高的吻合性和實用性。然后，將此方法與最小二乘法和神經(jīng)網(wǎng)絡兩種方法進行比較，利用同一樣本數(shù)據(jù)進行起重機載荷譜的獲取與預測，證明了支持向量機方法的優(yōu)越性。而且，本軟件操作簡單明了，使得操作人員不需要具備支持向量機的相關知識，就可以使用本軟件。最重要的是本研究成果為后續(xù)起重機疲勞壽命預測軟件的開發(fā)奠定了基礎。
[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.
【學位授予單位】：太原科技大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH21
[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.
【學位授予單位】：太原科技大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH21
【參考文獻】
相關期刊論文 前7條
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