Work

Physical information entropy-driven performance deterioration diagnosis for control systems of coal power plant

Year: 2025

Type: article

Abstract: This article primarily focuses on addressing the problem of performance deterioration diagnosis for coal power plant control systems. First, a digital twin dynamics model of real industrial control systems driven by physical state variables is constructed based on Newton’s second law. This digital twin is designed to achieve an accurate and lightweight digital description of the control system using two-dimensional orientations. Then, to enable physical information entropy-driven performance deterioration diagnosis (PIE-PDD) of the digital twin dynamic model, an adaptive overcomplete dictionary is established using the K-singular value decomposition algorithm in a lower triangular sparse structure. Furthermore, low-rank representation learning based on digital twin (DTLRR) is used to develop the PIE-PDD for control systems. This process aims to diagnose, identify, and perform root cause analysis of the performance degradation of the physical control system at the dynamic model level. Finally, recent results are compared with other deterioration diagnosis methods presented in this paper, and an experiment involving a coal mill primary air damper temperature control system is conducted to verify the effectiveness of the proposed method.

Source: The International Journal of Advanced Manufacturing Technology

DOI: 10.1007/s00170-025-16366-1

Authors: Ke Li, Yinsong Wang, Guang Wang, Hamid Reza Karimi, Weikang Zhan

Institutions: North China Electric Power University, Politecnico di Milano

Language: English

Topic: Fault Detection and Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $3,990