Detection and Classification of Oscillations in Process Control Loops Using Deep Learning Techniques

ABSTRACT Oscillatory behavior in process control loops is a persistent challenge in industrial plants, often resulting in diminished control performance, increased energy consumption, and economic losses. If left unaddressed, such oscillations can propagate throughout the plant, causing variability in downstream processes and negatively impacting throughput and product quality. Accurate detection and classification of oscillations, along with identification of their root causes, are therefore critical for enabling timely corrective actions that enhance control performance and overall process efficiency. Common causes of oscillations in process control include control valve stiction, suboptimal PID tuning, measurement noise, and external disturbances, each imparting distinct dynamic patterns on the process variable (PV) and controller output (OP). Manual detection and classification of these oscillations through visual analysis is time‐consuming and impractical due to the large number of control loops in modern plants. In this paper, we present an automated deep learning framework for the detection and classification of oscillations in process control loops. The proposed method employs a one‐dimensional convolutional neural network (1D‐CNN) to analyze time‐series data from PV and OP signals, enabling the model to learn and distinguish between different oscillation patterns associated with various root causes. The framework is trained and validated using both simulated datasets and real industrial plant data, ensuring robustness across a wide range of oscillation scenarios. Case studies are provided to illustrate the practical application of the method, and results demonstrate that the proposed approach achieves high accuracy in both detecting the presence of oscillations and correctly identifying their underlying causes. This automated solution offers a scalable and efficient tool for plant operators and engineers, supporting proactive maintenance, improved control loop reliability, and enhanced overall plant performance.