From Large Chemical Plant Data to Fault Diagnosis Integrated to Decentralized Fault-Tolerant Control: Pulp Mill Process Application

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• 作者：David Zumoffen ; Marta Basualdo
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2008
• 出版时间：February 20, 2008
• 年：2008
• 卷：47
• 期：4
• 页码：1201 - 1220
• 全文大小：1418K
• 年卷期：v.47,no.4(February 20, 2008)
• ISSN：1520-5045

文摘

In this paper, a new monitoring system is proposed by connecting different research areas, such as statisticalmonitoring, as well as knowledge-based and history-based systems. Tools such as adaptive principal componentsanalysis (APCA), fuzzy-logic (FL) methods, and artificial neural network (ANN) methods are integrated todevelop an efficient fault detection, isolation, and estimation (FDIE) system, especially for large chemicalplants. It is capable of detecting, classifying, and estimating several faulty process elements. The informationgiven by this new monitoring system is able to support the proper decisions for connecting and transformingan existing decentralized control strategy to a fault-tolerant method, based on an on-line reconfiguration.Thus, the obtained FDIE system is a valuable tool that is able to improve the overall performance of largeand complex nonlinear controlled plants. In this case, inherent faults in sensors and actuators are analyzed.The FDIE system is tested for single as well as sequential abnormal events on a pulp mill benchmark, whichis one of the biggest processes in the fault-tolerant control (FTC) that is integrated into the FDIE areas analyzedin the literature. A complete set of simulation results, evaluated by different indexes, together with cost analysisabout the process operational profits with and without an FDIE system, are used here, to demonstrate theeffectiveness of the proposed methodology.