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

Wu, P ORCID: 0000-0002-2729-9669, Yu, Y, Zhang, X ORCID: 0000-0002-8293-7539, Lou, S ORCID: 0000-0001-6611-4754, Gao, J ORCID: 0000-0002-7837-7559, Zhang, Q ORCID: 0000-0002-0651-469X and Yang, C ORCID: 0000-0002-4362-2104 (2025) Dynamical Component Extraction based Fault Detection for Industrial IoT With Application to Ironmaking Process. IEEE Internet of Things Journal, PP (99). p. 1. ISSN 2327-4662

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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 T 2 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.

Item Type:	Article
Uncontrolled Keywords:	46 Information and Computing Sciences; 4007 Control Engineering, Mechatronics and Robotics; 40 Engineering; 4010 Engineering Practice and Education; 9 Industry, Innovation and Infrastructure; 0805 Distributed Computing; 1005 Communications Technologies; 40 Engineering; 46 Information and computing sciences
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Engineering
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date of first compliant Open Access:	23 January 2026
Date Deposited:	23 Jan 2026 13:07
Last Modified:	23 Jan 2026 13:07
DOI or ID number:	10.1109/JIOT.2025.3643465
URI:	https://researchonline.ljmu.ac.uk/id/eprint/27968

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