Simultaneous fault diagnosis in chemical plants using a multil-abel approach

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. (C) 2007 American Institute of Chemical Engineers.