Shape-Based Pattern Recognition Approaches toward Oscillation Detection

Oscillation in control loops is a frequent problem in the process industries. These oscillations directly impact product quality, leading to a decreased plant profit. Additionally, oscillations increase energy consumption and waste raw materials and pose a significant restriction on the performance of the operational unit. Therefore, it is essential to isolate the loops that exhibit oscillations. In this work, two practical and effective methods are proposed to detect oscillations in process control loops. Both methods are aimed at detecting the presence of a triangle-like shape in the "D vs process variable (PV)" [or "D vs controller output (OP)"] plot to identify oscillations in the control loops. Here, "D" represents the Euclidean distance, which involves the deviations from the mean values of the variables OP and PV. Method 1 accomplishes this objective in an unsupervised manner by fitting a nonlinear algebraic function to the data: "D" and "PV". Method 2 uses a deep convolutional neural network for detecting the triangle-like shape. The performance of both the methods was evaluated by applying them to benchmark control loops sourced from various industries, including chemical, paper, mining, and metal industries, along with control loops in a local refinery unit. While both methods have their own advantages and application scenarios, the results demonstrated that both the proposed methods identified oscillatory control loops for the majority of the cases studied.