Evaluation of the Extreme Learning Machine for automatic fault diagnosis of the Tennessee Eastman chemical process

The Extreme Learning Machine is an attractive artificial neural network architecture due to its low computational cost during the training process. In this work this classifier architecture is evaluated in the context of automatic fault diagnosis. As a benchmark, the data provided by the Tennessee Eastman simulator is used. The results are compared to the Support Vector Machine, K-Nearest Neighbor classifiers and methods based on feature extraction techniques, like e.g. Principal Component Analysis, Partial Least Squares, Independent Component Analysis. The test results suggest that the Extreme Learning Machine is an attractive alternative classification method of process conditions.