Multichannel profile-based monitoring method and its application in the basic oxygen furnace steelmaking process

Abstract

Many industrial processes are equipped with a large number of sensors, which usually generate multichannel high-dimensional profiles that can be used to monitor the health condition and detect anomalies of the processes. However, the data irregularity, information obscurity, complex correlations, and nonlinear structures of the multichannel data pose significant challenges for the development of anomaly detection methodologies. To address these challenges, this article proposes a method, Mahalanobis Distance-based Functional Derivative Support Vector Data Description (MD-FDSVDD), for the process monitoring of applications with multichannel profiles. The proposed method first estimates a smooth function of each profile from its irregularly acquired observations and then takes its derivative function to enhance the characteristics associated with anomalies. Next, the smoothed derivative functions are transformed based on Mahalanobis distance to address the strong linear correlation challenge. Finally, the transformed derivative data are used to construct a functional SVDD model to detect anomalies. The effectiveness of the proposed method is evaluated using a simulated dataset and a real-world dataset from a Basic Oxygen Furnace steelmaking process.