Work

Digital Twin-Driven Physically Constrained Generative Adversarial Network for Industrial Boiler Fault Diagnosis

Year: 2025

Type: article

Abstract: As an important energy equipment, industrial boilers require more reliable fault diagnosis technology to ensure their continuous safe operation under high temperature and high pressure conditions. Due to the difficulty in collecting fault data, traditional fault diagnosis methods based on a large amount of labeled fault data cannot be effectively applied to actual industrial scenarios. This paper proposes a physical constrained generative adversarial network (PCGAN) driven by digital twins (DT) for industrial boiler fault diagnosis, which can accurately diagnose boiler faults when training data is insufficient. First, this paper uses DT technology to establish a virtual model of the boiler and uses the constructed DT model to emulate the failure conditions of the boiler and generate simulated data. Then, a novel PCGAN fault diagnosis method is designed, and data obtained by the DT model is used to train and validate its effectiveness. Finally, the feasibility of the proposed method in industrial boiler fault diagnosis is verified by experimental results. The results show that the fault diagnosis accuracy of this methods reaches 96.28%, which is significantly higher than other data-driven fault-diagnosis methods.

Source: IEEE Transactions on Instrumentation and Measurement

DOI: 10.1109/tim.2025.3558806

Authors: Qibing Wang, Xinhan Zhang, Jiawei Lu, Gang Xiao, Yaxing Ren, Wenjian Li

Institutions: China Jiliang University, University of Lincoln

Language: English

Topic: Fault Detection and Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,645