【摘要】：隨著全球工業(yè)智造的大行其道,人們對工業(yè)生產系統(tǒng)的穩(wěn)定性、工業(yè)生產運行過程的經濟性及產品質量等各個方面的要求愈加嚴格。工業(yè)自動化市場規(guī)模的急劇擴張使得現(xiàn)代工業(yè)系統(tǒng)和設備愈加復雜,要保證大型復雜工業(yè)系統(tǒng)正常運行,需要面臨諸多挑戰(zhàn)。因此,為實現(xiàn)對工業(yè)過程實時有效地監(jiān)控與檢測,確保生產過程的安全可靠,利用支持向量機方法對對非線性工業(yè)過程的大數據進行故障檢測與預測具有重要的理論價值和實際意義。本文分析了支持向量機的基礎理論,推導了該算法的建模原理和過程。針對非線性工業(yè)過程中大數據的故障檢測和預測,首先采用交叉驗證優(yōu)化方法對支持向量機進行核參數優(yōu)化。然后分別利用支持向量機、主成分分析法和增強偏最小二乘法對連續(xù)攪拌釜式加熱器過程進行故障檢測,并對各個算法的故障檢測結果進行分析比對,實驗結果表明,SVM分類器在實際復雜工業(yè)過程中具有優(yōu)異的預測能力和理想的運行時間。針對非線性工業(yè)過程的故障預測問題,通過學習半監(jiān)督學習方法,利用孿生支持向量機和改進算法(S~4VM)對工業(yè)過程的故障狀態(tài)進行有效地預測分析。S~4VM對初始參數設定值不敏感,能同時考慮多個候選大邊界低密度分界線,并在最壞情況下優(yōu)化標簽分配,在解決非線性工業(yè)過程大數據的故障預測的問題上表現(xiàn)優(yōu)異。
[Abstract]:With the popularity of global industrial intelligence, the requirements for the stability of industrial production system, the economy of industrial production process and the quality of products are becoming more and more stringent. The rapid expansion of industrial automation market makes modern industrial systems and equipment more complex. To ensure the normal operation of large-scale complex industrial systems, many challenges need to be faced. Therefore, in order to realize the real-time and effective monitoring and detection of the industrial process and ensure the safety and reliability of the production process, The support vector machine (SVM) method is of great theoretical value and practical significance for the fault detection and prediction of big data in nonlinear industrial processes. In this paper, the basic theory of support vector machine is analyzed, and the modeling principle and process of the algorithm are deduced. Aiming at the fault detection and prediction of big data in nonlinear industrial process, the kernel parameters of support vector machine are optimized by cross-validation optimization method. Then, support vector machine, principal component analysis and enhanced partial least square method are used to detect the faults of the continuous stirred tank heater, and the results of each algorithm are analyzed and compared. The experimental results show that the SVM classifier has excellent prediction ability and ideal running time in complex industrial processes. In order to solve the problem of nonlinear industrial process fault prediction, by learning semi-supervised learning method, twinning support vector machine and improved algorithm (S~4VM) are used to effectively predict the fault state of industrial process and analyze that Sch _ 4VM is insensitive to the initial parameter setting value. It can simultaneously consider multiple candidate large boundary low density boundaries and optimize label assignment in the worst case. It is excellent in solving the problem of big data's fault prediction in nonlinear industrial processes.
【學位授予單位】：渤海大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP277

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本文編號：2231705