Research on dynamic anomaly diagnosis of control loop based on clustering algorithm and t‐SNE visualization

Abstract Since abnormal working conditions or early equipment failures usually cause abnormal performances of critical control loops, loop abnormality must be detected effectively. Motivated by the intrinsic complexity of control loop dynamics, our study introduces a novel scheme that integrates classification and visualization techniques to delve into their dynamic characteristics. The scheme contains correlation analysis, K‐means clustering, and t‐distribution stochastic neighbour embedding (t‐SNE). Firstly, dynamic characteristics from historical data of both manipulated and controlled variables within the control loop were extracted. Subsequently, the correlation analysis method was employed to identify the finite impulse response model. Secondly, various sets of finite impulse response model parameters were compiled into a data matrix for further analysis using the K‐means algorithm to cluster the data effectively. Next, the dimensionality of the labelled data matrix was reduced using t‐SNE for visualization purposes. The scaling process iterated until distinct boundaries emerged for each category, labelling them based on a predefined threshold. Finally, in the online phase, anomalies are diagnosed using the parameters of the finite impulse response model derived from real‐time data, comparing them with the scaled offline model set. The effectiveness of our scheme is validated through the application of real‐world data from a Chinese refinery to identify anomalies within the control loop. Furthermore, our scheme demonstrates superior accuracy when compared with traditional techniques such as principal component analysis (PCA), isometric mapping (ISOMAP), independent component analysis (ICA), and multiscale wavelet transform.