Anomaly detection based on one-class intelligent techniques over a control level plant

A large part of technological advances, especially in the field of industry, have been focused on the optimization of productive in processes. However, the detection of anomalies has turned out to be a great challenge in fields like industry, medicine or stock markets. The present work addresses anomaly detection on a control level plant. We propose the application of different intelligent techniques, which allow to obtain one-class classifiers using real data taken from the correct plant operation. The performance of each classifier is assessed and validated with real created faults, achieving successful overall results.