Fault diagnosis based on support vector machines and systematic comparison to existing approaches

An innovative data based fault diagnosis system (FDS) using Support Vector Machines (SVM) is applied on a standard chemical process. Besides its simpler design and implementation, this technique allows dealing better with complex and large data sets. For that reason, it was expected to improve usual pattern classifiers performance reported in chemical engineering literature, such as artificial neural networks or PCA modeling techniques. In order to compare results with previously reported works, a standard case study such as the Tennessee Eastman (TE) process benchmark was considered and SVM achieved consistent and promising results. Besides, the difficulties encountered when comparing the results reported are discussed and a FDS comparison methodology is proposed based on reliability and accuracy of each FDS. In that sense, this study establishes a reference framework for future comparisons.