Simultaneous fault diagnosis in chemical plants using Support Vector Machines

One of the main limitations of the current plant supervisory control systems is the correct management of multiple simultaneous faults, which is crucial for supporting plant operators decision-making. In this work, Support Vector Machines (SVM) are used because of its proved efficiency dealing with multiclass problems in other technical areas. A Fault Diagnosis System has been developed implementing a multilabel approach using SVM and has been tested addressing a difficult diagnosis problem widely studied in the literature, the Tennessee Eastman process. Successful results have been obtained when diagnosing up to four simultaneous faults. These very first results are very promising since they have been achieved without any data processing or parameter tuning. Furthermore, they have been obtained just using training sets consisting of single faults, thus proving the achievement of a very powerful learning capacity.