Digital twin enhanced fault diagnosis reasoning for autoclave

Yucheng Wang, Fei Tao (), Ying Zuo, Meng Zhang and Qinglin Qi
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Yucheng Wang: Beihang University
Fei Tao: Beihang University
Ying Zuo: Beihang University
Meng Zhang: Tsinghua University
Qinglin Qi: Beihang University

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 6, No 24, 2913-2928

Abstract: Abstract Autoclave is the most important equipment in the composite curing process, and its real-time condition has a direct impact on the quality of composite materials. Therefore, rapid and precise fault diagnosis reasoning is of great significance for the autoclave. To address the shortage of signed directed graph (SDG)-based fault diagnosis method, this paper proposes a fault diagnosis method based on digital twin (DT) enhanced SDG for autoclave. Firstly, the SDG model of autoclave temperature control system is constructed, and the model is improved and enhanced by pre-fault transition state identification, fuzzy confirmation of node states, and simplification of potential branch circuits by using DT. The effectiveness of the method in this paper is verified by fault diagnosis based on SDG and DT-SDG methods respectively. The experimental results show that the method proposed in this paper can improve the speed and resolution of fault diagnosis by reducing the number of potential fault propagation paths and the number of inferences.

Keywords: Digital twin; Fault diagnosis; Autoclave; Signed directed graph; Reasoning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02174-5

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