Dynamical Component Extraction based Fault Detection for Industrial IoT With Application to Ironmaking Process

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Ping Wu, Yicheng Yu, Xujie Zhang, Siwei Lou, Jinfeng Gao, Qian Zhang, Chunjie Yang

Published: 01 Jan 2025, Last Modified: 07 Jan 2026IEEE Internet of Things JournalEveryoneRevisionsCC BY-SA 4.0
Abstract: The Industrial Internet of Things (IIoT) has become a crucial infrastructure in the process industry, particularly in the era of Industry 4.0. Ensuring operational safety in industrial processes necessitates fault detection techniques, which play a pivotal role in IIoT systems. These systems continuously collect high-dimensional process data, which often exhibit dynamic behavior due to the inherent complexity of industrial operations. Consequently, the dynamic characteristics of such data pose significant challenges for fault detection. As a powerful dimensionality reduction technique, Dynamical Component Analysis (DyCA) decomposes multivariate measurements of a dynamical system into a deterministic component which can be described by a system of differential equations and independent noise components. DyCA incorporates the covariance matrices of both the signals, and their derivative, as well as their cross-correlation. By doing so, it identifies a low-dimensional subspace that minimizes the error in the underlying ordinary differential equations. The DyCA components are estimated to capture low-dimensional trajectories that characterize the process dynamics. This study proposes a novel data-driven fault detection method based on dynamical component analysis for dynamic processes. Leveraging these DyCA components that represent the low-dimensional trajectories to describe the process dynamics, Hotelling’s T2 and Square Prediction Error (SPE) statistics are utilized as monitoring metrics for fault detection. Case studies on the widely utilized Tennessee Eastman process benchmark and a real-world blast furnace ironmaking process are conducted to demonstrate the effectiveness and capability of the proposed DyCA based fault detection method, comparing its performance with other relevant methods.