in Wiley InterScience (www.interscience.wiley.com).One of the main limitations of current plant supervisory control systems is the reliability and the correct management of simultaneous faults, which is crucial for supporting the plant operators' decision making. In this work, a MultiLabel approach that makes use of support vector machines as the learning algorithm is employed to arrange a novel fault diagnosis system (FDS). The FDS is trained to address a difficult control case study from industry widely studied in the literature, the Tennessee Eastman process. Successful results have been obtained when diagnosing up to four simultaneous faults. These results are very promising since they have been obtained by just using simple training sets consisting of single faults, thus proving a very high learning capacity.
A fault diagnosis system is developed by integrating principal component analysis (PCA) with fuzzy logic knowledge-based (FLKB) systems. A PCA model is created using normal state data. Then it is used to project normal and faulty data during the training stage. It evaluates the process variables values and their correlation, allowing fast and reliable fault detection. Once detection is performed, a FLKB system is used to evaluate the contributions of each variable to changes in the process, finding the root causes of the abnormal event detected. A simple methodology to automatically extract compact process information is presented. Then, an optimization algorithm is implemented to improve the isolation performance. The methodology is demonstrated in an academic case study and in the Tennessee Eastman process benchmark.
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