在本研究中我們藉由分析含有耦合前饋及回饋迴路系統的失誤傳遞行為，提出以模糊邏輯為基礎之失誤診斷策略。此方法需以兩階段執行：首先在離線準備階段中我們以失誤樹分析來找出所有可能造成系統危害的失誤來源，並藉由分析系統的有向圖模式預測每一誤源的失誤傳遞路徑以及線上可觀察到的症候發生順序，再根據上述分析找出所有可能產生的症候型式以及型式演化程序，這些不同程序可被用來建構兩層式的模糊推論統。而在接下來線上診斷的階段中，我們藉由擷取即時測量訊號並依模糊推論系統中的IF THEN法則計算出誤源的發生可能性指標，最後以系統案例研究之模擬結果來驗證此方法的可行性。

　By considering the fault propagation behaviors in process systems with coupled feed forward and feedback loops, a fuzzy-logic based fault diagnosis strategy has been developed in the present work.The proposed methods can be implemented in two stages. In the off-line preparation stage, the root causes of a system hazard are iden-tified by determining the minimal cut sets of the corresponding fault tree. The occu-rrence order of observable disturbances caused by each fault origin is derived from the system digraph. All possible patterns of the on-line symptoms and their evolution sequences can then be deduced accordingly. These sequences are used as the basis for constructing a two-layer fuzzy inference system. In the next on-line implementation stage, the occurrence indices of the the root causes are computed with the IF-THEN rules embedded in the inference engine using the real-time measurement data. Simu-lation studies have been carried out to demonstrate the feasibility of the proposed approach.

Chang, C. T., Chen, J. W. Implementation issues concerning the ekf-based fault diagnosis technique. Chemical Engineering Science 50, 2861, 1995.

Chang, S. Y., Chang, C. T. A fuzzy-logic based fault diagnosis strategy for process control loops. Chemical Engineering Science 58, 3395–3411, 2003.

Chang, S. Y., Lin, C. R., Chang, C. T. A fuzzy diagnosis approach using dynamic fault trees. Chemical Engineering Science 57, 2971–2985, 2002.

Dash, S., Rengaswamy, R., Venkatasubramanian, V. Fuzzy-logic based trend classifi-cation for fault diagnosis of chemical process. Computer and Chemical Engineering 27, 347–362, 2003.

Horowitz, E., Sahni, S. Fundamentals of DATA STRUCTURE IN PASCAL. Computer
Science Press, 1984.

Hoskins, J. C., Kalivur, K. M., Himmeblau, D. M. Fault diagnosis in complex chemicalplants using artificial neural networks. A.I.Ch.E. Journal 37, 137, 1991.

Iri, M., Aoki, K., O’Shima, E., Matsuyama, H. An algorithm for diagnosis of system failure in the chemical process. Computers Chemical Engineering 3, 489, 1979.

Ju, S. N., Chen, C. L., Chang, C. T. Construting fault trees for the advanced process control systems-an application to the cascade control loops. IEEE Trans. Reliab 53,43–60, 2004.

Mathworks, 2002a. Fuzzy logic toolbox-user guid. The Mathworks Inc.

Mathworks, 2002b. SIMULINK-dynamic system simulation for MATLAB-using simulink. The Mathworks Inc.

Maurya, M. R., Rengaswamy, R., Venkatasubramanian, V. Application of signed
diagraph-based analysis for fault diagnosis chemical process flowsheets. Engineering Aplication of Atrifical Intelligence 17, 501–518, 2004.

Petti, T. F., Klein, J., Dhurjati, P. S. Diagnostic model processor: Using deep knowledge for process fault dianosis. A.I.Ch.E. Journal 36, 565, 1990.

Sugeno, M. Industrial applications of fuzzy control. Elsevier Science Pub. Co, 1985.

Tarifa, E. E., Scenna, N. J. Falut diagnosis for msf dynamic states using a sdg and fuzzy logic. Desalination 166, 93–101, 2004.

Ulerich, N. H., Powers, G. J. Online hazard aversion and fault diagnosis in chemical processes: The digraph + fault-tree method. IEEE Trans. Reliab 37, 171–177, 1988.

Zhang, Z. Q.,Wu, C. G., Zhang, B. K., Xia, T., Li, A. F. Sdg multiple fault diagnosis by real-time inverse inference. Reliability Engineering and System Safety 87, 173–189,2005.