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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃孝平(Hsiao-Ping Huang) | |
dc.contributor.author | Kuo-Yuan Luo | en |
dc.contributor.author | 羅國元 | zh_TW |
dc.date.accessioned | 2021-06-12T18:09:53Z | - |
dc.date.available | 2007-11-15 | |
dc.date.copyright | 2007-11-15 | |
dc.date.issued | 2007 | |
dc.date.submitted | 2007-11-04 | |
dc.identifier.citation | [1] Abu-el-zeet, Z. H., Becerra, V. M. and Roberts, P. D. “Combined Bias and Outlier Identification in Dynamic Data Reconciliation,” Comput. Chem. Eng., 26, 921-935, 2002.
[2] Ali, Y. and Narasimhan, S. “Sensor Network Design for Maximizing Reliability of Linear Processes,” AIChE J., 39, 2237-2249, 1993. [3] Ali, Y. and Narasimhan, S. “Sensor Network Design for Maximizing Reliability of Bilinear Processes,” AIChE J., 42, 2563-2575, 1996. [4] Alici, S. and Edgar, T. F. “Nonlinear Dynamic Data Reconciliation via Process Simulation Software and Model Identification Tools,” Ind. Eng. Chem. Res., 41, 3984-3992, 2002. [5] Almasy, G. A. and Sztano, T. “Checking and Correction of Measurements on The Basis of Linear System Model,” Probl. Contr. Inform. Theor., 4, 57–69, 1975. [6] Almasy, G. A. “Principles of Dynamic Balancing,” AIChE J., 36, 1321-1330, 1990. [7] Almasy, G. A. and Mah, R. S. H. “Estimation of Measurement Error Variances from Process Data,” Ind. Eng. Chem. Proc. Des. Dev., 23, 779-784, 1984. [8] Albuquerque, J. S. and Biegler, L. T. “Data Reconciliation and Gross-Error Detection for Dynamic Systems,” AIChE J., 42, 2841-2856, 1996. [9] Bagajewicz, M. “A Review of Techniques for Instrumentation Design and Upgrade in Process Plants,” Can. J. Chem. Eng., 80, 3-16, 2002. [10] Bagajewicz, M. J. and Jiang, Q. “Integral Approach to Plant Dynamic Reconciliation,” AIChE J., 43, 2546-2558, 1997. [11] Bagajewicz, M. J. and Sánchez, M. “On the Impact of Corrective Maintenance in the Design of Sensor Networks,” Ind. Eng. Chem. Res., 39, 977-981, 2000. [12] Bagajewicz, M. J. and Sánchez, M. “Reallocation and Upgrade of Instrumentation in Process Plants,” Comput. Chem. Eng., 24, 1961-1980, 2000. [13] Bagajewicz, M. J. and Sánchez, M. “Cost-Optimal Design of Reliable Sensor Networks,” Comput. Chem. Eng., 23, 1757-1762, 2000. [14] Bakshi, B. R. “Multiscale Analysis and Modeling Using Wavelets,” J. Chemometr., 13, 415-434, 1999. [15] Bakshi, B. R. and Stephanopoulos, G. “Representation of Process Trends. III. Multiscale Extraction of Trends from Process Data,” Comput. Chem. Eng., 18, 267-302, 1994. [16] Becerra, V. M., Roberts, P. D. and Griffiths, G. W. “Dynamic Data Reconciliation for Sequential Modular Simulators: Application to A Mixing Process,” Proc. Am. Control Conf., 2740-2744, Chicago, 2000. [17] Bhushan, M. and Rengaswamy, R. “Design of Sensor Network Based on the Signed Directed Graph of the Process fpr Efficient Fault Diagnosis,” Ind. Eng. Chem. Res., 39, 999-1019, 2001. [18] Bhushan, M. and Rengaswamy, R. “Comprehensive Design of a Sensor Network for Chemical Plants Based on Various Diagnosability and Reliability Criteria. 1. Framework,” Ind. Eng. Chem. Res., 41, 1826-1839, 2002. [19] Bhushan, M. and Rengaswamy, R. “Comprehensive Design of a Sensor Network for Chemical Plants Based on Various Diagnosability and Reliability Criteria. 2. Applications,” Ind. Eng. Chem. Res., 41, 1840-1860, 2002. [20] Bruce, A. G., Donoho, D. L., Gao, H. Y. and Martin, R. D. “Denoising And Robust Nonlinear Wavelet Analysis,“ In: Proceedings of SPIE on Wavelet Applications in Signal and Image, 2242, 325-336, 1994. [21] Bruce, A. and Gao, H. Y. 'WaveShrink: Shrinkage Functions and Thresholds,' SPIE, 2569, 270-281, 1995. [22] Burrus, C. S., Gopinath, R. A. and Guo, H. Introduction to Wavelets and Wavelet Transformations- A Primer, Prentice-Hall, Upper Saddle River, U.S.A., 1998. [23] Chang, W. and Lee, T. Y. “Dynamic Data Reconciliation Considering Model Structure Uncertainty,” J. Chem. Eng. Japan, 34, 176-184, 2001. [24] Chen, J., and Romagnoli, J. A. “A Strategy for Simultaneous Dynamic Data Reconciliation and Outlier Detection,” Comput. Chem. Eng., 22, 559-562, 1998. [25] Cohen, A., Daubechies, I. and Vial, P. 'Wavelets on the Interval and Fast Wavelet Transforms,” Appl. Comput. Harmonic Anal., 1, 54-81, 1993. [26] Coifman, R. R. and Donoho, D. L. “Translation Invariant De-noising,” In: Antoniadis A. and Oppenheim, G. (Eds.), Wavelets and Statistics, Springer-Verlag, New York, 125-150, 1995. [27] Crowe, C. M. 'Reconciliation of Process Flow Rates by Matrix Projection. II: The nonlinear case,' AIChE J., 32, 616-623, 1986. [28] Crowe, C. M. “Recursive Identification of Gross Errors in Linear Data Reconciliation,” AIChE. J., 34, 541-550, 1988. [29] Crowe, C. M. “The Maximum-power Test for Gross Errors in The Original Constraints in Data Reconciliation,” Can. J. Chem. Eng., 70, 1030-1036, 1992. [30] Crowe, C. M. “Data Reconciliation Progress and Challegnges,” J. Process Control, 6, 89-98, 1996. [31] Crowe, C. M., Garcia Campos, Y. A. and Hrymak, A. 'Reconciliation of Process Flow Rates by Matrix Projection,' AIChE J., 29, 881-888, 1983. [32] Dahmen, W. and Michelli, C. A. “Using the Refinement Equation for Evaluaating Integrals of Wavelets,” SIAM J. Numer. Anal. 30, 507-537, 1993. [33] Darouach, M., Ragot, J., Fayolle, J. and Maquin, D. “Data Validation in Large-scale Steady-state Linear Systems,” World Cong. On Scientific Computation, IMACS-IFAC, Paris, 1988. [34] Darouach, M. and Zasadzinski, M. “Dara Reconciliation in Generalized Linear Dynamic Systems, AIChE J., 37, 193-201, 1991. [35] Daubechies, I. Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics, Philadelphia, Pa., 1992. [36] Daubechies, I. “Orthonormal Bases of Compactly Support Wavelets. Commun. Pure Appl. Math., 41, 909-996, 1988. [37] Donoho, D. L. “Nonlinear wavelet methods for recovering signals, images, and densities from indirect and noisy data,” Proceedings of Symposia Appl. Math., 173-205, 1993. [38] Donoho, D. L., “De-noising by Soft-Thresholding,” IEEE Trans. Inf. Theory, 41, 613-627, 1995. [39] Donoho, D. L. and Johnstone, I. M. “Ideal Spatial Adaptation by Wavelet Shrinkage,” Biometrika, 81, 425–455, 1994. [40] Donoho, D. L. and Johnstone, I. M. “Adapting to Unknown Smoothness via Wavelet Shrinkage,” J. Am. Stat. Assoc., 90, 1200-1224, 1995. [41] Donoho, D. L. and Johnstone, I. M. “Minimax Estimation via Wavelet Shrinkage,” Ann. Stat. 26, 879-921, 1998. [42] Donoho, D. L., Johnstone, I. M., Kerkyacharian, G. and Picard, D. “Wavelets shrinkage: Asymptotia?,” J. Roy. Stat. Soc. B, 57, 301-369, 1995. [43] Donoho, D. L., Johnstone, I. M., Kerkyacharian, G. and Picard, D. “Density Estimation by Wavelet Thresholding,” Ann. Stat., 24, 508–539, 1996. [44] Doymaz, F., Bakhtazad, A., Romagnoli, J. A. and Palazoglu, A. “Wavelet-based Robust Filtering of Process Data,” Comput. Chem. Eng., 25, 1549-1559. 2001. [45] Dunia, R., Qin, S. J., Edgar, T. F. and McAovy, T. J. “Identification of Faulty Sensors Using Principal Component Analysis,” AIChE J., 42, 2797-2812, 1996. [46] Flehmig, F., Watzdorf, R. V. and Marquardt, W. “Identification of Trends in Process Measurements Using The Wavelet Transformation,” Comput. Chem. Eng., 22, 491-496, 1998. [47] Gao HY, 'Threshold Selection in WaveShrink,' Technical report, StatSci Division, MathSoft, Inc., 1700 Westlake Ave. N, Seattle, WA98109, 1997 [48] Gelb, A. Applied Optimal Estimation, MIT Press, Cambridge, Mass., 1974. [49] Girshick, M. A., and Rubin, H. “A Bayes Approach to a Quality Control Model,” Ann. Math. Stat., 23, 114–125, 1952. [50] Grewal, M. S. and Andrews, A. P. Kalman Filtering: Theory and Practice Using MATLAB, John Wiley and Sons, New York, U.S.A., 2001. [51] Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., Stahel, W. Robust statistics: the approach based on influence functions, Wiley, New York, 1986. [52] Hayward, S. “Constrained Kalman Filter for Least-squares Estimation of Time-varying Beamforming Weights,” In: McWhirter, J. and Proudler, I. (Eds.), Mathematics in Signal Processing IV, Oxford University Press, New York, 113-125, 1998. [53] Heinonen, P. and Neuvo, Y. “FIR-Median Hybrid Filters,” IEEE Trans. Signal Process. ASSP-35, 832-838, 1987. [54] Hlavacek, V. “Analysis of a Complex Plant - Steady State and Transient Behaviour. I Plant Data Estimation and Adjustment,” Comput. Chem. Eng., 1, 75-100, 1977. [55] Hodouin, D. and Everell, M. D. “A Hierarchical Procedure for Adjustment and Material Balancing of Mineral Processes Data,” Int. J. Miner. Proc., 7, 91-116, 1980. [56] Jensen, A. Cour-Harbo, A. Ripples in Mathematics-The Discrete Wavelet Transform. Springer-Verlag, Berkin Heidelberg, 2001. [57] Kalman, R. E. “New Approach to Linear Filtering and Prediction Problems,” J. Basic Eng., ASME, 82, 35-45, 1960. [58] Kao, C. S., Tamahane, A. C. and Mah, R. S. H. “Gross Error Detection in Serially Correlated Process Data,” Ind. Eng. Chem. Res., 31, 254-262, 1992. [59] Karjala, T. W. and Himmelblau, D. M. “Dynamic Rectification of Data via Recurrent Neural Network and the Extended Kalman Filter,” AIChE J., 42, 2225-2239, 1996. [60] Kim, I. M., Liebman, M. J. and Edgar, T. F. “A Sequential Error in Variables Estimation Method for Nonlinear Dynamic Systems,” Comput. Chem. Eng., 15, 663–670, 1991. [61] Kim, I. M., Liebman, M. J. and Edgar, T. F. “Robust Error in Variables Estimation Using Nonlinear Programming Techniques,” AIChE J., 36, 985–993, 1990. [62] Knepper, J. C. and Gorman, J. W. “Statistical Analysis of Constrained Data Sets,” AIChE J., 26, 260–264, 1980. [63] Kong, M., Chen, B. and Li, B. “An integral approach to dynamic data rectification,” Comput. Chem. Eng., 24, 749-753, 2000. [64] Kong, M., Chen, B. and He, X. “Wavelet-Based Regulation of Dynamic Data Reconciliation,” Ind. Eng. Chem. Res., 41, 3405-3412, 2002. [65] Kretsovalis, A. and Mah, R. S. H. 'Observability and Redundancy Classification in Generalized Process Networks-I. Theorems,' Comput. Chem. Eng., 12, 671-687, 1988. [66] Kretsovalis, A. and Mah, R. S. H. 'Observability and Redundancy Classification in Generalized Process Networks-II. Algorithms,' Comput. Chem. Eng., 12, 689-703, 1988. [67] Kuehn D. R. and Davidson, H. “Computer Control II: Mathematics of Control,” Chem. Eng. Prog., 57, 44-47, 1961 [68] Liang, J. and Parks, T. W. “A Two-dimensional Translation Invariant Wavelet Representation and Its Applications,” In: Proceedings of IEEE International Conference on Image Processing, 1994 [69] Liebman, M. J. and Edgard, T. F. “Data reconciliation for Nonlinear Processes,” In: Proceedings of AIChE Annual Meeting, Washington, 1988. [70] Liebman, M. J., Edgar, T. F. and Lasdon, L. S. “Efficient Data Reconliliation for Dynamic Processes Using Nonlinear Programming Techniques,” Comput. Chem. Eng., 16, 963-986, 1992. [71] Luo, K. and Huang, H. “A Wavelet Enhanced Integral Approach to Linear Dynamic Data Reconciliation,” J. Chem. Eng. Jpn., 38, 1035-1048, 2005. [72] Madron, F. Process Plant Performance-Measurement and Data Processing for Optimization and Retrofits, Ellis Horwood series on Chemical Engineering, D. Sharp ed., Ellis Horwood, Chichester, U.K., 1992. [73] Madron, F. and Veverka, V. 'Optimal Selection of Measuring Points in Complex Plants by Linear Models,' AIChE J., 38, 227-236, 1992. [74] Mah, R. S. H., Stanley, G. M. and Downing, D. W. “Reconciliation and Rectification of Process Flow and Inventory Data,” Ind. Eng. Chem. Proc. Des. Dev., 15, 175–183, 1976. [75] Mah, R. S. H. “Design and Analysis of Performance Monitoring Systems,” In: Seborg, D. E. and Edgar, T. F. (Eds.), Chemical Process Control II, Engineering Foundation, New York, 525-540, 1982. [76] Mah, R. S. H. Chemical Process Structures and Information Flows, Butterworths Series in Chemical Engineering, Stoneham, Boston, U.S.A., 1990. [77] Mah, R. S. H. and Tamhane, A. C. “Detection of Gross Errors in Process Data,” AIChE J., 28, 828-830, 1982. [78] Mallat, S. G., “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell., 11, 674-693, 1989. [79] Mallat, S. G. A Wavelet Tour of Signal Processing, Academic, San Diego,Calif., 1999. [80] Mallat, S. G. and Zhong, S., “Characterization of Signals from Multiscale Edges,” IEEE Trans. Pattern Anal. Mach. Intell., 14, 710-732, 1992. [81] McBrayer, K. and Edgar, T. F. “Bias Detection and Estimation in Dynamic Data Reconciliation,” J. Process Control, 5, 285–289, 1995. [82] Misiti, M., Misiti,Y., Oppenheim, G. and Poggi, J. M. Wavelet Toolbox for Use with MATLAB® :User’s Guide Version 2, The MathWorks, Inc., Apple Hill Drive Natick, MA., 1997. [83] Muske, K. and Edgar, T. F. Nonlinear State Estimation, Prentice Hall, Englewood Cliffs, NJ, 1998. [84] Narasimhan, S. and Jordache, C. Data Reconciliation and Gross Error Detection-An Intelligent Use of Process Data, Gulf Publishing Company, Houston, U.S.A., 2000. [85] Narasimhan, S. and Mah, R. S. H. “Generalized Likelihood Ratio Method for Gross Error Identification,” AIChE J., 33, 1514–1521, 1987. [86] Nounou, M. N. and Bakshi, B. R. “Online Multiscale Filtering for Random And Gross Errors without Process Models,” AIChE J., 45, 1041-1058, 1999. [87] Pollak, M. “Average Run Lengths of an Optimal Method of Detecting a Change in Distribution,” Ann. Stat., 15, 749-779, 1987. [88] Porrill, J. “Optimal Combination and Constraints for Geometrical Sensor Data,” Int. J. Robot. Res., 7, 66-77, 1988. [89] Pratt, W. K. Digital Image Processing, John Wiley and Sons, Inc., New York, 2001. [90] Reilly, P. M. and Carpani, R. E. “Application of Statistical Theory to Adjustment of Material Balances,” In: 13th Can. Chem. Eng. Conf., Montreal, Quebec, 1963. [91] Ripps, D. L. “Adjustment of Experimental Data,” Chem. Eng. Prog. Symp. Ser., 61, 8–13, 1965. [92] Rollins, D. K., and Davis, J. F. “Unbiased Estimation of Gross Errors in Process Measurements,” AIChE J., 38, 563–572, 1992. [93] Rollins, D. K. and Devanathan, S. “Unbiased Estimation in Dynamic Data Reconciliation,” AIChE J., 39, 1330-1334, 1993. [94] Romagnoli, J. A. and Sánchez, M. C. Data Processing and Reconciliation for Chemical Process Operations, Academic Press, San Diego, U.S.A., 2000. [95] Sánchez, M. and Romagnoli, J. 'Use of Orthogonal Transformations in Data Classification-Reconciliation,' Comput. Chem. Eng., 20, 483-493, 1996. [96] Sánchez, M., Romagnoli, J., Jiang, Q. and Bagajewicz, M. “Simultaneous Estimation of Biases and Leaks in Process Plants,” Comput. Chem. Eng., 23, 841-857, 1999. [97] Simon, D. and Chia, T. L. “Kalman Filtering with State Constraints,” IEEE Trans. Aerosp. Electron. Syst., 38, 128-136, 2002. [98] Simpson, D. E., Voller, V. R. and Everett, M. G. “An Efficient Algorithm for Mineral Processing Data Adjustment,” Int. J. Miner. Proc., 31, 73–96, 1991. [99] Smith, S. W. The Scientist and Engineer's Guide to Digital Signal Processing, California Technical Publishing, San Diego, Calif., 1999. [100] Stanley, G. M. and Mah, R. S. H. “Eatimation of Flows and Temperatures in Process Networks,” AIChE J., 23, 642-650, 1977. [101] Strang, G. “Wavelets and Dilation Equations: A Brief Introduction,” SIAM Review, 31, 614-627, 1989. [102] Strang, G. “Wavelet Transform versus Fourier Transforms,” Bull. Amer. Math. Soc., 28, 288-305, 1993. [103] Strang, G. Wavelets and Filter Banks, Wellesley-Combridge Press, Wellesley, MA., 1996. [104] Tamhane; A. C. and Mah, R. S. H. “Data Reconciliation and Gross Error Detection in Chemical Process Networks,” Technometrics, 27, 409-422, 1985. [105] Tona, R. V., Benqlilou, C., Espuna, A. and Puigjaner, L. “Dynamic Data Reconciliation Based on Wavelet Trend Analysis,” Ind. Eng. Chem. Res., 44, 4323-4335, 2005. [106] Tong, H. and Crowe, C. M. “Detection of Gross Errors in Data Reconciliation by Principal Component Analysis,” AIChE J., 41, 1712–1722, 1995. [107] Tukey, J. W. 'Nonlinear (nonsuperposable) Methods for Smoothing Data,' Congr. Rec., 673-681, 1974. [108] Vachhani, P., Rengaswamy, R., Gangwal, V., and Narasimhen, S. “Recursive Estimation in Constrained Nonlinear Dynamical Systems,” AIChE J., 51, 946-959, 2005. [109] Vaclavek, V. 'Studies on System Engineering III. Optimal Choice of The Balance Measurements in Complicated Chemical Systems,' Chem. Eng. Sci., 24, 947-955, 1969. [110] Weeks, M. Digital Signal Processing using MATLAB and Wavelets, Infinity Science Press, Mass., 2007. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/27562 | - |
dc.description.abstract | 本研究利用離散小波轉換與數據調和理論處理動態系統之數據調和問題。在處理動態數據調和問題時,可考慮以下列兩種方式進行。第一種方式為先將系統動態方程式轉換成代數方程式,接著以數學規劃法方式求得調和估測值;第二種方法則是藉由卡門濾波器(Kalman Filter)相關之估測法同步處理系統動態與狀態估測問題。
研究中提出以不同方式應用離散小波轉換於上述之動態數據調和方法,藉此達到數據調和之目的。利用離散小波轉換主要目的為分析、過濾量測訊號,並解決在處理動態調和問題時所遇到之缺點與不足,如多項式方法(Polynomial Approach)不適於處理變動較大之動態訊號等等。 文章主要分成三個部分。在第一部分,考慮線性動態系統情況下,以辛普森積分法將系統動態方程式轉換成代數方程式,接下來可利用穩態數據調和理論解決此動態數據調和的問題。應用離散小波轉換可以降低轉換時所產生之誤差,並可以預先過濾充滿雜訊之量測訊號。 在第二部份,利用卡門濾波估測的方法具有時間遞迴方式演算式的優點,適合應用於線上即時動態數據調和的問題,但在利用卡門濾波方法估測時,在擴增的狀態變數(Augmented State Variables)常會帶有較大的誤差,故在此部分提出以離散小波轉換過濾為基礎之線上過濾方法應用於卡門濾波估測法,藉此獲得更佳之估測效果。 第三部分中以離散小波理論中之尺度函數(Scaling Function)去近似量測訊號及其微分,藉此將系統動態方程式轉換成代數方程式後藉由數學規劃法處理動態數據調和之問題。在最佳化參數之過程中,搜尋之值域為在尺度函數之係數所在之值域,如此可減少最佳化搜尋變數之數目,並可達到動態數據調和之目的。 研究中並提出針對單一重大錯誤之偵測與隔離方法,可有效地偵測出系統中之重大錯誤。在文中將以範例說明所提出之各項理論,最後並討論與比較所提出三種方法之優缺點。 | zh_TW |
dc.description.abstract | In this dissertation, methodologies are proposed to solve dynamic data reconciliation problems by using discrete wavelet transform. As dealing with the dynamic data reconciliation problems, two approaches are usually considered: By the first approach, system differential equations must be transformed to algebraic equations in advance and the problems are solved by the mathematical programming method in the following; by the second approach, Kalman filter method is used to deal with system dynamic equations along with the estimation simultaneously.
In the research, discrete wavelet transform theories are applied to the above-mentioned two approaches to accomplish the reconciliation. Wavelets theories are aimed to analyze and filter the measurement signals, which can deal with some shortcomings and deficiencies, e.g. the polynomial approach has difficulty as encountering the tortuous signals, in the data reconciliation problems. There are three main parts in the research. In the first part, considering a linear dynamic system, system’s differential algebraic equations are converted into algebraic equations using Simpson’s integration rule, which can be solved easily by the steady-state data reconciliation theories for this dynamic data reconciliation problem. Discrete wavelets transform is used to filter the measurement signals to decrease the errors from the transformation. In the second part, on-line dynamic data reconciliation approach is proposed by using the Kalman filter estimation which has time-recursive formulations. Augmenting the states is always needed in this approach which may result significant errors for reconciliation. Thus, an on-line filtering using the discrete wavelets transform is applied to filter the measurements from a real time process for reconciliation. Some difficulties, e.g. the boundary problems, encountered in the wavelets filtering are overcome by a proposed robust algorithm. A single gross error detection and isolation method is also proposed which can isolate the fault successfully. In the third part, through using the scaling functions in the wavelets theories, the measurement signals and their derivatives are approximated and the dynamic data reconciliation problem is formulated and solved by optimization via mathematical programming method. As optimization, the searching space for is the domain of the scaling function coefficients, which can reduce the number of the searching variables. Examples are performed to illustrate the proposed approaches and summaries are given for the comparisons of the proposed three methods. | en |
dc.description.provenance | Made available in DSpace on 2021-06-12T18:09:53Z (GMT). No. of bitstreams: 1 ntu-96-D91524010-1.pdf: 1643891 bytes, checksum: a43e9290bbf1867b810496e8b736d2fe (MD5) Previous issue date: 2007 | en |
dc.description.tableofcontents | 致 謝 I
摘 要 III Abstract V Contents VII List of Figures XI List of Tables XIII Acronyms XV Chapter 1 Introduction 1 1.1. Overview 1 1.2. Filtering Methods without Models 3 1.3. Filtering Methods with Models 7 1.4. Dynamic Data Reconciliation (DDR) by Wavelets Approaches 12 1.5. Research Objectives and Motivations 13 1.6. Organization 14 Chapter 2 Concepts of Data Reconciliation (DR) 15 2.1. Overview 15 2.1.1. Types of errors 15 2.1.2. Statistical basis of DR problem 17 2.2. Linear Steady-State Data Reconciliation (SSDR) 19 2.2.1. Formulation 19 2.2.2. Lagrange multipliers 20 2.3. Redundancy and Observability 21 2.4. Variance-covariance Calculation for SSDR 23 2.5. Non-linear Data Reconciliation 24 2.6. Dynamic Data Reconciliation (DDR) 25 2.6.1. Kalman filtering (KF) 25 2.6.2. Constrained Kalman filtering 27 2.6.3. Extended Kalman filtering (EKF) 28 2.6.4. DDR based on mathematical programming 29 2.7. Gross Error Detection (GED) 31 Chapter 3 Concepts of Wavelet Analysis 35 3.1. Overview 35 3.1.1. Multi-scale time-frequency representations 35 3.1.2. Short term Fourier transform 37 3.1.3. Types of wavelets 37 3.1.4. Two-parameter functions 39 3.1.5. Properties of wavelets 40 3.2. Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) 42 3.3. DWT for Finite Length Signal 46 3.4. DWT and IDWT in Matrix Form 48 3.5. Scaling Mother Function 50 3.6. The Derivative of Scaling Mother Function 52 Chapter 4 Dynamic Data Reconciliation by Integral Approach 57 4.1. Introduction 57 4.2. Wavelets Filtering 58 4.2.1. DWT analysis and filtering 58 4.2.2. Band-pass frequency of DWT 59 4.2.3. Determining the DWT analysis level 61 4.3. Integral Approach by Simpson’s Rule 65 4.4. Gross-error Detection and Fault Isolation 69 4.5. Examples 70 4.5.1. Example 1 73 4.5.2. Example 2 80 4.6. Conclusions 82 Chapter 5 Constrained Kalman Filtering for Dynamic Data Reconciliation 85 5.1. Introduction 85 5.2. On-line Robust Wavelets Filtering 86 5.2.1. DWT and IDWT analysis for filtering 87 5.2.2. Determination the wavelets filtering level 88 5.2.3. Robust wavelets filtering method 90 5.2.4. Filtering examples 93 5.3. KF Approach for Reconciliation 98 5.3.1. Data reconciliation using constrained KF 98 5.3.2. Detection and isolation of gross errors 101 5.4. Examples 104 5.4.1. Illustration example of a four-tank system 104 5.4.2. Bias, leak detection and isolation 110 5.5. Conclusions 114 Chapter 6 Dynamic Data Reconciliation by Mathematical Programming 119 6.1. Introduction 119 6.2. Approximation by Scaling Functions 121 6.2.1. Expansion signal by scaling function 121 6.2.2. Expanding the derivative of signal 122 6.3. The Comparisons of Two Approaches 123 6.4. Reconciliation 124 6.4.1. Linear system 125 6.4.2. Non-linear system 130 6.5. Examples 130 6.5.1. Linear four-tank system 131 6.5.2. Continuous stirred-tank reactor (CSTR) system 136 6.6. Conclusions 143 Chapter 7 Conclusions and Future Works 147 7.1. Summary and Comparisons 147 7.2. Contributions 148 7.3. Future Works 150 A. Wavelets Decomposition and Reconstruction Matrices 151 B. Wavelets Differential Operator 154 Bibliography 157 | |
dc.language.iso | en | |
dc.title | 利用離散小波轉換於動態數據調和之研究 | zh_TW |
dc.title | On Dynamic Data Reconciliation with Discrete Wavelet Transform | en |
dc.type | Thesis | |
dc.date.schoolyear | 96-1 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 余政靖(Cheng-Ching Yu),陳誠亮(Cheng-Liang Chen),黃世宏(Shyh-Hong Hwang),周宜雄(Yi-Shyong Chou),鄭西顯(Shi-Shang Jang),陳榮輝(Jung-Hui Chen),黃奇(Chyi Hwang) | |
dc.subject.keyword | 離散小波轉換,動態數據調和, | zh_TW |
dc.subject.keyword | discrete wavelet transform,dynamic data reconciliation, | en |
dc.relation.page | 163 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2007-11-05 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 化學工程學研究所 | zh_TW |
顯示於系所單位: | 化學工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
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ntu-96-1.pdf 目前未授權公開取用 | 1.61 MB | Adobe PDF |
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