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研究生： 陳俊彥
Chen, Jun-Yen
論文名稱： 以定性模式為基礎之失誤診斷方法研究
Studies on Qualitative Model-Based Fault Diagnosis Methods
指導教授： 張珏庭
Chang, C. T.
學位類別： 博士
Doctor
系所名稱： 工學院 - 化學工程學系
Department of Chemical Engineering
論文出版年： 2009
畢業學年度： 97
語文別： 英文
論文頁數： 150
中文關鍵詞： 失誤診斷 、有號有向圖 、感測器的配置 、自動機 、模糊系統
外文關鍵詞： fault diagnosis, signed directed graph, sensor placement, automata, fuzzy
相關次數： 點閱：73  下載：2 
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    • 由於製程的尺度及複雜性急遽成長，失誤發生所造成的經濟利益及安全危害勢必更加嚴重，因此失誤診斷在化學程序中已成為一個不可缺少的任務。本論文探討兩個在失誤診斷上極為重要的議題。第一個議題針對誤源預測來做研究，第二個議題則是探討感測器的配置策略。對於第一個問題本文提出一種兩階段的策略，此兩階段分別為（1）線下準備階段（2）線上實作階段。在線下階段（1）中，本文採用以有號有向圖為基礎的程序，此程序可定性的預測當一失誤發生或多個失誤發生時所有可能的失誤徵兆及失誤演化順序並且依據此預測產生相對應的模糊推論系統。我們可將此程序大致分為下面幾個步驟:建構診斷器，預測失誤徵兆及失誤演化順序，生成語言，建構模糊推論系統。
     建構診斷器:首先利用有號有向圖模組來預測每一個系統狀態可能後續發生的事件並由初始的正常狀態遞迴地利用此預測來產生所有系統狀態。其中事件的預測不僅包括不同變數之間的傳播 (空間序列傳播) 亦包括同一變數間不同大小等級的傳播 (時間序列傳播)。此外我們可以轉換所有狀態及狀態間的事件為一組節點及邊以便構成狀態轉移圖或者稱之為自動機。最後若將自動機上線上無法測量的事件離去便可以獲得對應的診斷器。
     預測失誤徵兆及失誤演化順序:根據產生的診斷器我們可以觀察到所有的狀態既符合所有可能的失誤徵兆並且由初始狀態沿著事件所代表的邊直到穩態所形成的所有路徑正符合每一個可能的失誤演化序。
     生成語言:每一個由上一步驟產生的路徑皆包含了一連串的事件序列，我們可以將此事件序列視為一個字串，因此所有的路徑對應的所有字串即可構成一組正規語言。
     建構模糊推論系統:最後所有的狀態及字串可能被編碼成為兩種不同的“若則”法則。此兩種不同的法則分別代表了靜態失誤徵兆及動態失誤傳播順序的對應法則。根據這些法則我們可以建構出兩層式的模糊推論系統，其中每一層分別對應於上述的兩種“若則”法則。一旦模糊系統建構完成在線上階段（2）中只需將線上測量數據傳送到此系統即能給出每一失誤可能發生的指標。最後以五個實例來論證此法的可行性。
    此外針對第二個問題本文結合了有號有向圖以及失誤演化順序來改進文獻中所提出的策略。此方法依據失誤演化順序來增加額外的資訊以便提供更好的感測器選擇。此資訊主要依據“兩個誤源若含有不同演化順序既可能用此不同順序的測量值來辨別此兩個不同的源”，因而這些測量值便可能形成感測器的候選位置。為了將此額外訊息併入文獻所提出的方法，本文亦提出了轉換此訊息到雙分矩陣的程序並且修正了文獻所提出了貪婪演算法以適用於擴張型的雙分矩陣。除此之外本文也嘗試以整數線性規畫的方式來解決此一問題並以三個實例來論證此兩種解法的可行性以及差異性。

    Due to the increase of chemical process scale and complexity, unpredicted fault will influence the economic interest and the process safety even more than it did before. Therefore, on-line fault diagnosis system should be considered as an indispensable tool in modern chemical process. Two fault diagnosis problems are studied in this thesis. First problem concerns about fault isolation, and the second problem concerns about sensor placement strategy. For the first problem, a method with two stages is proposed, i.e.,(1) the off-line preparation stage and (2) the on-line implementation stage. On stage (1), a SDG-based reasoning procedure is presented in this thesis to qualitatively predict all possible symptom patterns and also their evolution sequences caused by one or more fault propagating in any given process system as well as constructing the corresponding fuzzy inference system. This procedure consists of the following steps: generation of diagnoser, generation of symptom patterns and evolution sequences, generation of language and construction of fuzzy inference system.

    generation of diagnoser: The SDG model is used to predict all possible propagated events under a system state and recursively suggest all possible states derived from the initial normal state. Notice the predicted propagated events not only involve the propagation between different variables (spatial propagation) but also the propagation between different magnitudes of a same variable (temporal propagation). Consequently, these states and predicted events between states can be represented as nodes and edges of state-transition digraph, i.e., automata. In addition, the automata can also be converted into a diagnoser by
    deleting all unobservable measurements.

    generation of symptom patterns and evolution sequences: From the diagnoser, the corresponding possible symptom patterns as well as evolution sequence which represents the path from initial state to the final state caused by the fault are generated in this step.

    generation of language: Every path constructed by the previous step contains one consecutive events sequence, which can be identified as a word or a sting. Later, strings corresponding to all the paths can be combined together to construct a formal language.

    construction of fuzzy inference system: Finally, all states in diagnoser and strings can be encoded into two different kinds of IF-THEN rules. These two kinds of IF-THEN rules represent the static fault symptoms(candidate patterns) and the dynamic fault symptoms (evolution sequences)respectively. From these rules a comprehensive fuzzy inference system can be constructed for the online diagnosis purpose. The comprehensive fuzzy inference system consists of two layer fuzzy structure. The first layer represents the static fault diagnosis and the second layer represents the dynamic fault diagnosis. On stage (2), online measurements are fed into the fuzzy inference system constructed on stage (1) to show the possible outcome caused by every fault occurred. The feasibility of the proposed method is demonstrated here by using five different examples.

    For the second problem, a SDG-based method is proposed to design the sensor network on the basis of qualitatively predicted fault evolution sequences. This method is able to improve the original strategy proposed in the literature by taking the fault evolution sequences into consideration as additional information to assist in selecting sensor. The information is based on "If two fault origins contain different evolution sequences, the measurements of those different sequences can be used to distinguish those two different fault origins" principle and able to determine the placement of the sensors. To combine this additional information to the existing method proposed by literature, this thesis provide procedures to convert the information into bipartite matrix and revise the existing greedy algorithm using expanded bipartite matrix. Furthermore, to achieve a maximum level of resolution and, at the same time, to ensure observability, the corresponding design problems are also formulated as integer linear programs. The feasibility of the proposed method along with two different strategies, i.e., integer programs and greedy algorithm, is demonstrated with three different examples.

    1 Introduction . . . . . . . . . . . . . . . . . . . . . .12 1.1 Background . . . . . . . . . . . . . . . . . . . . . .12 1.2 Literature Review . . . . . . . . . . . . . . . . . . 14 1.2.1 Studies on SDG-based fault diagnosismethods . . . . 14 1.2.2 Studies on sensor placement strategies . . . . . . .16 1.3 Research Objectives . . . . . . . . . . . . . . . . . 17 1.4 Implementation Procedure . . . . . . . . . . . . . . .17 2 Prediction and Representation Methods of Fault Propagation Behaviors . . . . . . . . . . . . . . . . . . 22 2.1 Qualitative SimulationProcedure . . . . . . . . . . .22 2.2 System Automata . . . . . . . . . . . . . . . . . . 23 2.3 Diagnoser and Diagnosability . . . . . . . . . . . . .31 2.4 Fault Propagation Path . . . . . . . . . . . . . . . 32 2.5 Symptom Occurrence Order . . . . . . . . . . . . . . 35 2.6 Language Generation . . . . . . . . . . . . . . . . . 36 2.7 Basic State Enumeration Algorithms . . . . . . . . . .38 2.8 Basic String Enumeration Algorithms . . . . . . . . . 41 2.9 A Simple Example . . . . . . . . . . . . . . . . . . .44 3 Construction Procedures of Fuzzy Inference Systems. . . 47 3.1 Fundamental Principles . . . .. . . . . . . . . . . . 47 3.2 Candidate Patterns . . . . . . . . . . . . . . . . . 47 3.2.1 Candidate patterns derived from a tree-shaped SDG . 48 3.2.2 Candidate patterns derived from a SDG with NFFLs . .48 3.2.3 Candidate patterns derived from a SDG with NFBLs . .48 3.3 Pattern Evolution Sequences . . . . . . . . . . . . . 49 3.4 Single-Layer Inference Rules . . . . . . . . . . . . .49 3.5 Two-Layer Inference Rules . . . . . . . . . . . . . . 50 3.6 On-line Computation Scheme . . . . . .. . . . . . . . 53 3.6.1 Membership functions of process deviations . . . . .53 3.6.2 Mamdani's mechanism . . . . . . . . . . . . . . . . 54 3.6.3 Sugeno's mechanism . . . . . . . . . . . . . . . . 56 3.7 A Simple Example . . . .. . . . . . . . . . . . . . . 57 4 Optimal Sensor Placement Strategy . . . . . . . . . . . 61 4.1 Research Justification . . . . . . . . . . . . . . . 61 4.2 Ordered Event Pairs . . . . . . . . . . . . . . . . . 61 4.3 Symptom Sets, Sensor Sets and Function Sets . . . . . 63 4.4 Bipartite Matrix . . . . . . . . . . . . . . . . . . 64 4.5 Optimal Sensor Placement Problem . . . . . . . . . . .65 4.5.1 Problem formulation . . . . . . . . . . . . . . . . 65 4.5.2 Solution procedures . . . . . . . . . . . . . . . . 66 4.6 Application Examples . . . . . . . . . . . . . . . . .69 4.6.1 Example 4.1 . . . . . . . . . . . . . . . . . . . . 69 4.6.2 Example 4.2 . . . . . . . . . . . . . . . . . . . . 71 4.6.3 Example 4.3 . . . . . . . . . . . . . . . . . . . . 73 5 Case Studies . . . . . . . . . . . . . . . . . . . . . .76 5.1 Example 5.1 . . . . . . . . . . . . . . . . . . . . . 76 5.2 Example 5.2 . . . . . . . . . . . . . . . . . . . . . 84 5.3 Example 5.3 . . . . . . . . . . . . . . . . . . . . . 89 5.4 Example 5.4 . . . . . . . . . . . . . . . . . . . . . 95 5.5 Example 5.5 . . . . . . . . . . . . . . . . . . . . . 99 5.6 Example 5.6 . . . . . . . . . . . . . . . . . . . . .104 6 Conclusions and Future Works . . . . . . . . . . . . .108 6.1 Specific Contributions . . . . . . . . . . . . . . . 108 6.2 Unsettled Issues . . . . . . . . . . . . . . . . . . 108 A Theorems . . . . . . . . . . . . . . . . . . . . . . . 118 A.1 Theorems concerning the total number of diagnoser state . . . . . . . . . . . . . . . . . . . . . . . . . .118 A.2 Theorem concerning the total number of event strings embedded in a single-level tree-shaped SOO . . . . . . . 121 A.3 Theorem concering the upper bounds of the total numbers of states and strings in a single-level tree-shaped SOO..123 B Algorithms . . . . . . . . . . . . . . . . . . . . . . 125 B.1 Basic state enumeration algorithm . . . . . . . . . .125 B.2 Basic string enumeration algorithm . . . . . . . . . 131 B.3 Enumeration algorithm for identifying all ordered event pairs in FPP . . . . . . . . . . . . . . . . . . . . . . 134 C Event strings embedded in Figure 2.10 . . . . . . . . .136 D Model notations used in the application examples of Chapter 4 . . . . . . . . . . . . . . . . . . . . . . . .139 D.1 Process variables in Example 4.1 . . . . . . . . . . 139 D.2 Process variables in Example 4.2 . . . . . . . . . . 140 D.3 SDG model of TE process in Example 4.3 . . . . . . . 141 E Process models . . . . . . . . . . . . . . . . . . . . 144 E.1 Mathematical model used for simulating single-tank Level-control system . . . . . . . . . . . . . . . . . . 144 E.2 Mathematical model used for simulating two-tank Level-control system . . . . . . . . . . . . . . . . . . . . . 144 E.3 Mathematical model used for simulating CSTR reactor system . . . . . . . . . . . . . . . . . . . . . . . . . 144 E.4 Mathematical model used for simulating single-tank system with feed forward control . . . . . . . . . . . . 145 E.5 Mathematical model used for simulating three tank system with parallel cascade control . . . . . . . . . . 145 F Process model parameters . . . . . . . . . . . . . . . 146 G Number of rules in examples . . . . . . . . . . . . . .150

    Ali, Y., Narasimhan, S., 1993. Sensor network for maximizing reliability of linear process. AIChE J. 39(5), 820.

    Ali, Y., Narasimhan, S., 1995. Redundant sensor network for maximizing reliability of linear process. AIChE J. 41(10), 820.

    Ali, Y., Narasimhan, S., 1996. Sensor network for maximizing reliability of bilinear process. AIChE J. 42(9), 2563.

    Bagajewicz, M., Carbrera, E., 2002. A new milp formulation for instrumentation network design and upgrade. AIChE J. 48(10), 2271.

    Bagajewicz, M., Fuxman, A., 2004. Instrumentation network design and upgrade for process monitoring and fault detection. AIChE J. 50(8), 1870.

    Bagajewicz, M., Sanchez, M., 1999a. Design and upgrade of non-redundant and redundant linear sensor networks. AIChE J. 45(9), 1927.

    Bagajewicz, M., Sanchez, M., 1999b. Duality of sensor network design models for parameter esitmation. AIChE J. 45(3), 661.

    Bagajewicz, M., Sanchez, M., 2000. Cost-optimal design of reliable sensor networks. Computers and Chemical Engineering 23(11/12), 1757.

    Bhushan, M., Narasimhan, S., Rengaswamy, R., 2008. Robust sensor network design for fault diagnosis. Computers and Chemical Engineering 32, 1067.

    Bhushan, M., Rengaswamy, R., 2000. Design of sensor network based on the signed directed graph of the process for e±cient fault diagnosis. Industrial & Engineering
    Chemistry Research 39, 999.

    Bhushan, M., Rengaswamy, R., 2002a. Comprehensive design of a sensor network for chemical plants based on various diagnosability and reliability criteria. 1. framework.
    Industrial & Engineering Chemistry Research 41, 1826.

    Bhushan, M., Rengaswamy, R., 2002b. Comprehensive design of a sensor network for chemical plants based on various diagnosability and reliability criteria. 2. applications. Industrial & Engineering Chemistry Research 41, 1840.

    Cassandras, C. G., Lafortune, S., 1999. Introduction to Discrete Event Systems. Kluwer Academic Publisher: Boston.

    Chang, C. C., Yu, C. C., 1990. On-line fault diagnosis using the signed directed graph. Industrial & Engineering Chemistry Research 29(7), 1290.

    Chang, S. Y., Chang, C. T., 2003. A fuzzy-logic based fault diagnosis strategy for process control loops. Chemical Engineering Science 58, 3395.

    Chang, S. Y., Lin, C. R., Chang, C. T., 2002. A fuzzy diagnosis approach using dynamic fault trees. Chemical Engineering Science 57, 2971.

    Chen, J., Howell, J., 2001. A self-validating control system based approach to plant fault detection and diagnosis. Computers and Chemical Engineering 25, 337.

    Chen, J., Howell, J., 2002. Towards distributed diagnosis of the TE process benchmark. Control Engineering Practice 10(9), 971.

    Chen, J., Yea, Y., Kong, C., 2006. Diagnosis of cascade control loop status using performance analysis based approach. Industrial & Engineering Chemistry Research
    45, 7540.

    Cormen, T. H., Leiserson, C. E., Rivest, R. L., Stein, C., 2001. Introduction to Algorithms, Second Edition. The MIT Press.

    Dash, S., Rengaswamy, R., Venkatasubramanian, V., 2003. Fuzzy-logic based trend classification for fault diagnosis of chemical process. Computers and Chemical Engi-
    neering 27, 347.

    Downs, J. J., Vogel, E. F., 1993. A plant-wide industrial process control problem. Computers and Chemical Engineering 17(3), 245.

    Frank, P. M., 1997. New developments using AI in fault diagnosis. Engng Applic. Artif. Intell. 10, 3.

    Fussell, J. B., 1974. Fault tree analysis - state of the art. IEEE Transactions on Reliability 23(1), 51.

    Gertler, J. J., 1998. Fault Detection and Diagnosis in Engineering Systems. Marcel Dekker, Inc.

    Iri, M., Aoki, K., OShima, E., Matsuyama, H., 1979. An algorithm for diagnosis of system failures in the chemical process. Computers and Chemical Engineering 3, 489.

    Isermann, R., Balle, P., 1996. Terminology in the field of supervision, fault detection and diagnosis. Accepted IFCA SAFE PROCESS Committee. Johnsonbaugh, R., Schaefer, M., 2004. Algorithms. Pearson Education: Upper Saddle
    River, NJ.

    Ju, S. N., Chen, C. L., Chang, C. T., 2004. Constructing fault trees for advanced process control systems - application to cascade control loops. IEEE Transactions on
    Reliability 53, 43.

    Kelly, B. E., Lees, F. P., 1986. The propagation of faults in process plants: 2. fault tree
    synthesis. Reliability Engineering 16(1), 39.

    Korbicz, J., Koscielny, J., Kowalczuk, Z. ,Cholewa W., 2004. Fault diagnosis: Models,
    Artifcial Intelligence, Applications. Springer-Verlag Berlin Heidelberg.

    Kotecha, P. R., Bhushan, M., Gudi, R. D., 2007. Constraint programming based robust sensor network design. Industrial & Engineering Chemistry Research 46(5), 5985.

    Kramer, M. A., Palowitch, B. L., 1987. A rule based approach to fault diagnosis using the signed directed graph. AIChE J. 33(7), 1068.

    Kuipers, B., 1994. Qualitative Reasoning. The MIT Press: Cambridge, MA.

    LaJoie, J., Lippman, S. B., 1998. C++ Primer, 3rd Edition. Addison Wesley Professional: Upper Saddle River.

    Lambert, H., 1997. Fault trees for location sensors in process systems. CEP Aug, 81.

    Lapp, S. A., Powers, G. A., 1977. Computer-aided synthesis of fault trees. IEEE Transactions on Reliability 26(1), 2.

    Lee, G., Tosukhowong, T., Lee, J. H., Han, C., 2006. Fault diagnosis using the hybrid method of signed digraph and partial least squares with time delay: The pulp mill
    process. Industrial & Engineering Chemistry Research 45, 9061.

    MacGregor, J. F., Kourti, T., 1995. Statistical process control of multivariate processes. Control Engineering Practice 3, 403.

    Madron, F., Veverka, V., 1992. Optimal selection of measurement points in complex plants by linear models. AIChE J. 38(2), 227.

    Mamdani, E., 1976. Advances in the linguistic synthesis of fuzzy controllers. International Journal of Man-Machine Studies 8, 669.

    Mamdani, E., 1977. Applications of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Transactions on Computers 26(12), 1182.

    Mamdani, E., Assilian, S., 1975. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7(1), 1.

    Maquin, D., Luong, M., Ragot, J., 1994. Observability analysis and sensor placement. SAFE PROCESS' 94 IFAC/IMACS Symposium on Fault Detection, Supervision and Safety for Technical Process, June 13-15, Espoo, Finland.

    Mathworks, 2006a. Fuzzy Logic Toolbox User Guid 2. The Mathworks Inc.

    Mathworks, 2006b. Simulink 6 Using Simulink. The Mathworks Inc.

    Maurya, M. R., Rengaswamy, R., Venkatasubramanian, V., 2007a. A signed directed graph and qualitative trend analysis-based framework for incipient fault diagnosis.
    Institution of Chemical Engineers 85(A10), 1407.

    Maurya, M. R., Rengaswamy, R., Venkatasubramanian, V., 2001. Systematic development and application of digraphs for process diagnosis and hazards analysis, in stephanopoulos, g., romagnoli, j.a. and yoon, e.s., (eds). 4th IFAC Workshop on On-Line Fault Detection and Supervision in the Chemical Process Industries
    (CHEMFAS-4).

    Maurya, M. R., Rengaswamy, R., Venkatasubramanian, V., 2003a. A systematic framework for the development and analysis of signed digraphs for chemical processes. 1.
    algorithms and analysis. Industrial & Engineering Chemistry Research 42(20), 4789.

    Maurya, M. R., Rengaswamy, R., Venkatasubramanian, V., 2004a. Application of signed digraph-based analysis for fault diagnosis chemical process °owsheets. Engineering Applications of Arti¯cial Intelligence 17, 501.

    Maurya, M. R., Rengaswamy, R., Venkatasubramanian, V., 2006a. A signed directed graph-based systematic framework for steady state malfunction diagnosis control
    loops. Chemical Engineering Science 61, 1790.

    Maurya, M. R., Rengaswamy, R., Venkatasubramanian, V., 2007b. Fault diagnosis using dynamic trend analysis: A review and recent developments. Engineering Applications of Arti¯cial Intelligence 20, 133.

    Maurya, M. R., Rengaswamyb, R., Venkatasubramaniana, V., 2003b. A systematic framework for the development and analysis of signed digraphs for chemical processes. 2. control loops and °owsheet analysis. Industrial & Engineering Chemistry Research 42(20), 4811.

    Maurya, M. R., Rengaswamyb, R., Venkatasubramaniana, V., 2004b. Application of signed digraphs-based analysis for fault diagnosis of chemical process °owsheets. Engineering Applications Artificial Intelligence 17(5), 501.

    Maurya, M. R., Rengaswamyb, R., Venkatasubramaniana, V., 2006b. Asigned directed graph-based systematic framework for steady-state malfunction diagnosis inside control loops. Chemical Engineering Science 61, 1790.

    McAvoy, T. J., Ye, N., 1994. Base control for the TE problem. Computers and Chemical Engineering 18(5), 383.

    Montgomery, D. C., 1985. Introduction to Statistical Quality Control. John Wiley and Sons: New York.

    Mylaraswamy, D., Kavuri, S., Venkatasubramanian, V., 1994.
    A framework for automated development of causal models for fault diagnosis. in AIChE Annual Meeting.

    Oyeleye, O., Finch, F., Kramer, M., 1989. Qualitative modeling and fault diagnosis of processes by MIDAS. Technical Report LIPSE-89-061. Department of Chemical
    Engineering, MIT, Cambridge, MA.

    Oyeleye, O. O., Kramer, M. A., 1988. Qualitative simulation of chemical process systems: steady-state analysis. AIChE J. 34, 1441.

    Petzold, C., 1998. Programming Windows. WA.

    Raghuraj, R., B. M., Rengaswamy, R., 1999. Location of sensors in complex chemical plants based on fault diagnostic observability criteria. AIChE J. 45(2), 310.

    Ricker, N. L., Lee, J. H., 1995a. Nonlinear model predictive control of the ten- nessee
    eastman challenge process. Computers and Chemical Engineering 19(9), 961.

    Ricker, N. L., Lee, J. H., 1995b. Nonlinear modeling and state estimation for the tennessee eastman challenge process. Computers and Chemical Engineering 19(9),
    983.

    Russell, E. L., Chiang, L. H., Braatz, R. D., 2000. Data-Driven Techniques for Fault Detection and Diagnosis in Chemical Processes. Springer-Verlag, London Berlin Hei-
    delberg.

    Sampath, M., Sengupta, R., Lafortune, S., 1996. Failure diagnosis using discrete-event models. IEEE Trans Cont Syst Tech. 4, 105.

    Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., Teneketzis, D., 1995. Diagnosability of discrete-event systems. IEEE Trans Auto Cont. 40, 1555.

    Schweizer, B., Sklar, A., 1963. Associative functions and abstract semi-groups. Publ. Math Debrecen 10, 69.

    Sen, S., Narasimhan, S., Deb, K., 1998. Sensor network design of linear processes using genetic algorithms. Computers and Chemical Engineering 22(3), 385.

    Shih, R. F., Lee, L. S., 1995a. Use of fuzzy cause-eect digraph for resolution fault diagnosis for process plants. 1. fuzzy cause-eect digraph. Industrial & Engineering
    Chemistry Research 34, 1688.

    Shih, R. F., Lee, L. S., 1995b. Use of fuzzy cause-eect digraph for resolution fault diagnosis for process plants. 2. diagnostic algorithm and application. Industrial &
    Engineering Chemistry Research 34, 1703.

    Shiozaki, J., Matsuyama, H., OShima, E., Iri, M., 1985. An improved algorithm for diagnosis of system failures in the chemical process. Computers and Chemical Engi-
    neering 9 (3), 285.

    Sugeno, M., 1985. Industrial Applications of Fuzzy Control. Elsevier Science Pub. Co.

    Tarifa, E. E., Scenna, N. J., 1997. Fault diagnosis, directed graphs, and fuzzy logic. Computers and Chemical Engineering 21, S649.

    Tarifa, E. E., Scenna, N. J., 1998. A methodology for fault diagnosis in large chemical processes and an application to a multistage °ash desalination process: part ii.
    Reliability Engineering and System Safety 60, 41.

    Tarifa, E. E., Scenna, N. J., 2003. Fault diagnosis for a MSF using a SDG and fuzzy logic. Desalination 152.

    Tarifa, E. E., Scenna, N. J., 2004. Fault diagnosis for MSF dynamic states using a SDG and fuzzy logic. Desalination 166, 93.

    Tsuge, Y., Shiozaki, J., Matsuyama, H., OShima, E., 1985. Fault diagnosis algorithms based on signed directed graphs and its modi¯cations. Indian Chemical Engineering
    Symposium Series 92, 133.

    Ulerich, N. H., Powers, G. A., 1988. Online hazard aversion and fault diagnosis in chemical processes: the digraph/fault tree method. IEEE Transactions on Reliability
    37(2), 171.

    Vedam, H., Venkatasubramanian, V., 1997. Signed digraph based multiple fault diagnosis. Computers and Chemical Engineering 21, S655.

    Vedam, H., Venkatasubramanian, V., 1999. PCA-SDG based process monitoring and fault diagnosis. Control Engineering Practice 7(7), 903.

    Venkatasubramanian, V., Rengaswamy, R., Kavuri, S., 2003b. A review of process fault detection and diagnosis, part II: Qualitative model and search strategies. Computers
    and Chemical Engineering 27, 313.

    Venkatasubramanian, V., Rengaswamy, R., Kavuri, S., Yin, K., 2003c. A review of process fault detection and diagnosis, part III: Process history based methods. Computers and Chemical Engineering 27, 327.

    Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S., 2003a. A review of process fault detection and diagnosis, part I: Quantitative model based methods.
    Computers and Chemical Engineering 27, 293.

    West, D. B., 2001. Introduction to Graph Theory. Prentice-Hall, Inc.: Upper Saddle River, NJ.

    Wilcox, N. A., Himmelblau, D. M., 1994a. Possible cause and effect graphs (PCEG) model for fault diagnosis I. methodology. Computers and Chemical Engineering
    18(2), 103.

    Wilcox, N. A., Himmelblau, D. M., 1994b. Possible cause and effect graphs (PCEG) model for fault diagnosis II. applications. Computers and Chemical Engineering 18(2), 117.

    Yager, R., 1980. On a general class of fuzzy connectives. Fuzzy Sets and Systems 4, 235.

    Zhang, Z., Wu, C., Zhang, B., Xia, T., Li, A., 2005. SDG multiple fault diagnosis by real-time inverse inference. Reliability Engineering and System Safety 87(2), 173.

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