부산대학교 도서관

In process-based industries, modern process control systems have become data-driven and collect a vast amount of data from sensors in the field and alarm information. The collected data provides an opportunity for the data scientist to learn from historical data and apply Machine Learning (ML) models to automate the process control systems. Thus, assisting the plant operators in making informed decisions. In this paper, we focus on the alarm prediction of control systems. Alarm prediction assists plant operators in observing the functioning of plants and taking corrective measures beforehand to avoid upcoming failure situations. A data pipeline is proposed in this paper comprising two approaches for alarm prediction. Both the approaches consider the alarm log simulated data from an industrial three-phase separator process typically found in oil fields. The first approach requires domain knowledge regarding the alarm thresholds values, and ML models are trained using the threshold values to perform alarm prediction. The second approach comprises ML models trained independently of the alarm threshold values, thus providing the alarm prediction time window. The alarm prediction time window provides the plant operator sufficient time to act on an impending failure. As the outcome from the two approaches is different, Long short-term memory (LSTM) is the best performing model for the first approach with an RMSE value of 0.03. For the second approach, a fully convolutional network (FCN) is the best performing model for time windows of 20, 40, 60, and 120 minutes, and LSTM is the best performing model with an accuracy 94% for the time window of 10 minutes.