本文選題：高含硫天然氣 + 多變量過程��； 參考：《重慶科技學院》2015年碩士論文

【摘要】：隨著現(xiàn)代工業(yè)系統(tǒng)復雜性和自動化程度不斷提高，被控過程同時發(fā)生物理化學反應和相位反應，涉及物質(zhì)轉(zhuǎn)化和能量傳遞，綜合受人、機、環(huán)、料、法不確定因素影響。整個生產(chǎn)過程表現(xiàn)不確定性、非線性、強耦合性、動態(tài)性等特點。傳統(tǒng)基于機理和過程特性的故障檢測與診斷方法受到極大限制�；跀�(shù)據(jù)驅(qū)動的過程監(jiān)控以反映系統(tǒng)運行狀況的數(shù)據(jù)為基礎，通過各種數(shù)據(jù)處理與分析手段，挖掘其內(nèi)在規(guī)律，在線檢測和識別過程中出現(xiàn)的異常操作和工況，追溯故障發(fā)生根本原因，從而為故障排查和系統(tǒng)恢復提供智能決策，最終保證復雜系統(tǒng)運行的可靠性和安全性。 目前，，高含硫天然氣凈化過程主要存在以下三個問題：一是天然氣處理量載荷波動會引起凈化系統(tǒng)模型參數(shù)發(fā)生遷移，從而導致靜態(tài)模型不能夠識別正常工況調(diào)整而發(fā)生監(jiān)控誤報警。二是高含硫天然氣凈化過程監(jiān)測數(shù)據(jù)結(jié)構(gòu)呈現(xiàn)非線性、非高斯性和時序自相關(guān)性特點，導致提取驅(qū)動凈化機理的過程參數(shù)顯得異常困難。三是提取出凈化過程的關(guān)鍵參數(shù)無法追溯原始參數(shù)貢獻度，從而難以實現(xiàn)故障診斷。 本文分別討論主元分析過程監(jiān)控和獨立分量分析過程監(jiān)控方法，并以美國田納西-伊斯曼模型為標準測試庫檢驗各種故障檢測與診斷方法性能，然后應用這些方法解決實際高含硫天然氣凈化過程故障檢測與診斷的問題。主要取得以下成果： 一是針對靜態(tài)模型無法識別工況調(diào)整而導致誤報警高問題，提出基于假設檢驗和動態(tài)確定算法的自回歸模型時滯階次確定方法，研究動態(tài)主元分析和動態(tài)獨立分量分析的監(jiān)控性能。二是針對非線性、非高斯性和動態(tài)工業(yè)過程，提出基于動態(tài)核獨立分量分析的故障檢測與診斷方法，實現(xiàn)復雜工業(yè)過程監(jiān)控。三是針對故障診斷困難，采用監(jiān)控統(tǒng)計量對原始參數(shù)的一階偏導數(shù)度量貢獻度，提出基于統(tǒng)計量的一階偏導數(shù)貢獻圖的故障診斷方法。 最后以高含硫天然氣凈化過程為研究對象，采用動態(tài)核獨立分量分析的故障檢測與診斷方法，達到很好的監(jiān)控性能效果；并針對故障診斷的異常參數(shù)，提出相應的安全控制措施。
[Abstract]:With the increasing complexity and automation of modern industrial system, the controlled process takes place in both physical and chemical reactions and phase reactions, which involve material transformation and energy transfer, and are comprehensively affected by uncertain factors such as human, machine, ring, material, and method.The whole production process is characterized by uncertainty, nonlinearity, strong coupling and dynamics.Traditional fault detection and diagnosis methods based on mechanism and process characteristics are greatly limited.The data-driven process monitoring is based on the data which reflects the system running condition. Through various data processing and analysis methods, the inherent rules are mined, and the abnormal operations and working conditions that appear in the process of on-line detection and identification are detected and identified.The root cause of the failure is traced back, which provides intelligent decision for fault troubleshooting and system recovery, and finally ensures the reliability and security of complex system operation.At present, there are three main problems in the purification process of high-sulfur natural gas: first, the fluctuation of natural gas treatment load will cause the migration of the model parameters of the purification system.As a result, the static model can not identify the normal condition adjustment and the monitoring error alarm occurs.The other is that the monitoring data structure of the purification process of high sulfur containing natural gas is nonlinear, non- and time series autocorrelation, which makes it very difficult to extract the process parameters that drive the purification mechanism.Third, the key parameters of the purification process can not be traced back to the original contribution, so it is difficult to achieve fault diagnosis.This paper discusses the principal component analysis (PCA) process monitoring and independent component analysis (ICA) process monitoring methods, and uses Tennessee Eastman model as the standard test library to test the performance of various fault detection and diagnosis methods.Then these methods are used to solve the problem of fault detection and diagnosis in the purification process of high-sulfur natural gas.The following results have been achieved:First, aiming at the problem of high false alarm caused by the adjustment of static model's unidentifiable working condition, a method of determining the time-delay order of autoregressive model based on hypothesis test and dynamic determination algorithm is proposed.The monitoring performance of dynamic principal component analysis and dynamic independent component analysis is studied.Secondly, aiming at nonlinear, non- and dynamic industrial processes, a fault detection and diagnosis method based on dynamic kernel independent component analysis (DKICA) is proposed to realize the monitoring of complex industrial processes.Thirdly, aiming at the difficulty of fault diagnosis, a fault diagnosis method based on the first order partial derivative contribution graph based on statistics is proposed by using the first order partial derivative of the monitoring statistics to measure the contribution degree of the original parameter.Finally, the method of fault detection and diagnosis based on dynamic kernel independent component analysis (DKIA) is used to study the purification process of high sulfur containing natural gas, which achieves a good monitoring performance effect, and aiming at the abnormal parameters of fault diagnosis,Put forward the corresponding safety control measures.
【學位授予單位】：重慶科技學院
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TE64

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