Detection of Intermittent Oscillation in Process Control Loops with Semi-Supervised Learning

Abstract

Oscillations in the control loops indicate the poor performance of the control loops. The occurrence of oscillations in the process control loop is quite high in the industry, so it needs to be reduced so that the control loop can work properly. The first step for oscillation reduction is oscillation detection. One type of oscillation that is difficult to detect is intermittent oscillation. The smart factory concept encourages the development of the intermittent oscillation detection system using machine learning by being implemented online. Therefore, in this study an online intermittent oscillation detection program is built using K-nearest neighbor (KNN)-based Semi-supervised learning (SSL) method. The SSL method applied is self-training. The training data was obtained by a simulation of the Tennessee Eastman Process. The data is segmented based on window size and extracted time series features. The extracted data is used to build a model to detect oscillations caused by stiction, tuning errors, and external disturbances in the reactor. The model is implemented online with sliding windows and MQTT. The best accuracy and F1-score of the model obtained are 96.15% and 95.15%. In online detection, the model detects the type of oscillation with an average time of 305 seconds.