本文選題：PCA + SVM��； 參考：《浙江工業(yè)大學(xué)》2012年碩士論文

【摘要】：隨著新的汽車(chē)涂裝生產(chǎn)技術(shù)、生產(chǎn)工藝以及大型復(fù)雜設(shè)備的不斷涌現(xiàn)和迅速發(fā)展,為了保證生產(chǎn)和設(shè)備高效、可靠的運(yùn)行,因此對(duì)汽車(chē)涂裝系統(tǒng)各機(jī)電設(shè)備運(yùn)行狀態(tài)的準(zhǔn)確診斷提出了更高的要求。由于智能理論的發(fā)展,設(shè)備狀態(tài)判別進(jìn)入了智能化發(fā)展階段。本文詳細(xì)研究了主成分分析法和支持向量機(jī)在涂裝線設(shè)備診斷中的應(yīng)用,并結(jié)合虛擬儀器進(jìn)行了涂裝線設(shè)備監(jiān)控與智能診斷系統(tǒng)的設(shè)計(jì)。論文主要內(nèi)容如下: 1.根據(jù)汽車(chē)涂裝線生產(chǎn)工藝,對(duì)各子系統(tǒng)主要設(shè)備的故障機(jī)理進(jìn)行了分析研究,指出了設(shè)備經(jīng)常發(fā)生的故障類(lèi)型和征兆,在各系統(tǒng)內(nèi)建立了數(shù)據(jù)采集系統(tǒng)。 2.研究了故障征兆提取技術(shù)。在實(shí)際環(huán)境下,眾多傳感器采集到的信號(hào),一方面,并不是所有變量都反映設(shè)備狀態(tài)的重要信息,有些會(huì)干擾診斷;另一方面,設(shè)備信號(hào)特征的輸出有一定相關(guān)性。因此,論文討論了主成分分析方法和改進(jìn)的主成分分析法的應(yīng)用。通過(guò)烘房燃燒加熱系統(tǒng)設(shè)備的實(shí)例對(duì)比分析,驗(yàn)證了此方法的優(yōu)勢(shì)。 3.較深入研究了核函數(shù)類(lèi)型及核參數(shù)對(duì)分類(lèi)器精度的影響。通過(guò)雙螺旋數(shù)據(jù)樣本仿真試驗(yàn),分別分析了高斯核和多項(xiàng)式核對(duì)分類(lèi)精度的影響,以及高斯核寬度系數(shù)和懲罰參數(shù)對(duì)分類(lèi)精度的影響,表明高斯核參數(shù)和懲罰參數(shù)在某個(gè)范圍時(shí),分類(lèi)器精度最好。 4.提出了一種基于主成分分析和支持向量機(jī)的設(shè)備狀態(tài)分類(lèi)識(shí)別方法。結(jié)合主成分分析法的特征提取和向量機(jī)的識(shí)別優(yōu)勢(shì),采用網(wǎng)格搜索交叉驗(yàn)證法尋求最優(yōu)核參數(shù),來(lái)建立向量機(jī)訓(xùn)練模型。通過(guò)烘房燃燒加熱系統(tǒng)4種設(shè)備的12種狀態(tài)進(jìn)行了驗(yàn)證。分別分析了主成分分析法改進(jìn)前后的分類(lèi)精度,識(shí)別率都基本達(dá)到85%以上。 5.結(jié)合虛擬儀器進(jìn)行了涂裝線設(shè)備監(jiān)控智能診斷系統(tǒng)的設(shè)計(jì),對(duì)數(shù)據(jù)采集、時(shí)域和頻域分析、特征提取和智能診斷等模塊進(jìn)行了介紹。 6.最后,對(duì)全文進(jìn)行了總結(jié),并對(duì)進(jìn)一步的研究提出一些展望。
[Abstract]:With the new auto painting technology, production technology and the rapid development of large and complex equipment, in order to ensure the efficient and reliable operation of the production and equipment, the accurate diagnosis of the operating state of the mechanical and electrical equipment of the automobile coating system is higher. The application of principal component analysis (PCA) and support vector machine (SVM) in the diagnosis of coating line equipment is studied in detail, and the design of monitoring and intelligent diagnosis system for coating line equipment is carried out with virtual instrument. The main contents of this paper are as follows:
1. according to the production process of the automobile coating line, the failure mechanism of the main equipment of each subsystem is analyzed and studied. The types and signs of the equipment often occur, and the data acquisition system is set up in each system.
2. the fault symptom extraction technology is studied. In the actual environment, the signals collected by many sensors, on the one hand, not all the variables reflect the important information of the equipment state, some will interfere with the diagnosis; on the other hand, the output of the signal features of the equipment has some relevance. The application of the sub analysis method is compared with the example of the heating system in the drying room, and the advantages of this method are verified.
3. the effect of kernel function type and kernel parameter on classifier precision is studied in depth. The influence of Gauss kernel and polynomial nucleation on classification accuracy is analyzed by double helix data sample simulation test, and the influence of Gauss kernel width coefficient and penalty parameter on classification precision are analyzed, and Gauss kernel parameter and penalty parameter are shown in a certain range. The classifier has the best accuracy.
4. a new method of equipment state classification and recognition based on principal component analysis and support vector machine is proposed. Combining the feature extraction of the principal component analysis and the recognition advantage of the vector machine, the grid search cross validation method is used to find the optimal kernel parameters, and the training model of the vector machine is established. The 12 states of the 4 equipment of the heating system are through the drying room combustion heating system. The classification accuracy is analyzed before and after the improvement of principal component analysis. The recognition rate is basically over 85%.
5. the design of the intelligent diagnosis system for the monitoring and control of the coating line equipment is carried out with the virtual instrument. The modules of data acquisition, time domain and frequency domain analysis, feature extraction and intelligent diagnosis are introduced.
6. finally, we summarize the full text and make some prospects for further research.
【學(xué)位授予單位】：浙江工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：U468.22;TH165.3

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本文編號(hào)：1980943