Graph-Frequency Domain Kalman Filtering for Industrial Pipe Networks Subject to Measurement Outliers

• Published in: IEEE transactions on industrial informatics Vol. 20; no. 5; pp. 7977 - 7985
• Main Authors: Su, Lei, Han, Zhongyang, Zhao, Jun, Wang, Wei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anomaly detection; Constrained Kalman filtering; Fourier transforms; Frequency domain analysis; graph signal processing (GSP); industrial pipe networks (PNs); Kalman filters; Mathematical models; measurement outliers; Outliers (statistics); Pipes; Pollution measurement; Smoothness; State estimation; Temperature distribution; Temperature measurement; time-varying graph; Time-varying systems
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2024.3369715

This article is concerned with the outlier-resistant state estimation problem for industrial pipe networks (PNs). PN signals, e.g., pressure and temperature, are modeled as low-pass time-varying graph signals, and an outlier-resistant graph-frequency domain (GFD) filter is proposed. This article extends the innovation saturation (IS) mechanism from the node domain to the GFD. A GFD IS function is developed with a more compact structure, which reduces outlier effects by restricting PN signals' smoothness. Since the PN signal is time-varying, the saturation function is embedded in the Kalman filter as an inequality constraint. The close-form solution to the filter is derived by replacing the nonlinear inequality constraints with high-pass graph Fourier transforms, and the cutoff frequency is found by analyzing the graph-frequency component of prior and posterior. A steel industrial PN is used to evaluate the proposal. Results indicate that the proposed filter exhibits superior performance in PN systems, particularly internal nodes.