Multi-objective Machine Learning for control performance assessment in PID control loops

Abstract

Control Performance Assessment (CPA) is a critical endeavor in industrial processes, ensuring optimal functioning of control systems. Traditionally, CPA has been addressed through solutions using some control performance indicators. Nowadays, the integration of data science and machine learning has emerged as a viable alternative, particularly in classification tasks related to CPA. That is, in a binary classification scheme, the goal is to predict whether incoming data from the control loop belongs to class 0 or 1, representing the absence or presence of an anomaly (performance degradation). In such a case, a trade-of between false positives and false negatives should be obtained, via the training phase of a given supervised machine learning structure for example. Usually, this is a conflicting trade-of, where multi-objective optimization techniques in the training phase of such learners could bring interesting results. In this paper, we explore the usability of multi-objective optimization training in machine learning, for control performance assessment classification. A database describing 30 control performance indicators (features) in a PID control loop is used. The obtained results indicate that the proposed approach could bring interesting applications to improve the performance of CPA classification systems.