Early classification of industrial alarm floods based on semi-supervised learning

Abstract

Early classification of ongoing alarm floods in industrial monitoring systems is crucial to provide a safe and efficient operation. In this article, a data-driven approach is proposed to address the early classification problem with unlabeled historical data. To prioritize earlier activated alarms and take advantage of the triggering time information of alarms, a vector representation called exponentially attenuated component (EAC) is used to represent alarm floods. This makes alarm sequences fit for different powerful machine learning algorithms. A method based on the time information of unlabeled historical alarm floods is formulated to determine the attenuation coefficient for EAC representation. With the Gaussian mixture model, an efficient semi-supervised approach is proposed to provide an early classification of alarm floods using unlabeled historical data. The efficiency of the proposed method is validated by the Tennessee Eastman process benchmark and a real industrial dataset.