Probabilistic assessment and automatic detection of oscillations in industrial control loops

Abstract

Loop oscillation is a prevalent issue in industrial control loops. Affected by changes in production tasks, loop load, and external environment, industrial control systems typically have more complex oscillation patterns. Industrial signals often exhibit multimodal superposition, noise interference, and non-stationarity. Binary judgment of oscillation is prone to false alarms or missed detections in industrial control loops. More fundamentally, the binary classification framework fails to quantify oscillation risks. Therefore, complex oscillations in industrial control loops still need a more flexible assessment framework. In this paper, a probabilistic assessment framework for oscillations is proposed from the perspective of the statistical characteristics of zero-crossings. To enhance the reliability of signal preprocessing, adaptive VMD and significant IMFs identification are combined. By incorporating the statistical characteristics for coefficient of variation into regularity test of oscillation, the conventional binary classification is transformed into the probabilistic assessment. In simulation studies, the effectiveness of adaptive VMD, significant IMFs identification, and probabilistic assessment in complex signals and negative feedback loops is verified through three examples. In industrial scenario studies, the performance of the proposed method is analyzed in 93 benchmark industrial loops. The proposed method is compared with 12 distinct methods. The detection results of benchmark industrial loops show that the performance of this method is superior to most detection methods. The proposed method can not only ensure high accuracy, sensitivity and specificity, but also evaluate and grade the oscillation probability without historical data and model training. This detection method provides reference value for risk classification and decision optimization of process monitoring.