Digital Twin for rotating machinery fault diagnosis in smart manufacturing

By Jinjiang Wang, Lunkuan Ye, Robert X. Gao, Chen Li, Laibin Zhang. International Journal of Production Research, 2018

463 citations recorded by the source corpus.

ABSTRACT With significant advancement in information technologies, Digital Twin has gained increasing attention as it offers an enabling tool to realise digitally-driven, cloud-enabled manufacturing. Given the nonlinear dynamics and uncertainty involved during the process of machinery degradation, proper design and adaptability of a Digital Twin model remain a challenge. This paper presents a Digital Twin reference model for rotating machinery fault diagnosis. The requirements for constructing the Digital Twin model are discussed, and a model updating scheme based on parameter sensitivity analysis is proposed to enhance the model adaptability. Experimental data are collected from a rotor system that emulates an unbalance fault and its progression. The data are then input to a Digital Twin model of the rotor system to investigate its ability of unbalance quantification and localisation for fault diagnosis. The results show that the constructed Digital Twin rotor model enables accurate diagnosis and adaptive degradation analysis.

Subfields: Industrial and Manufacturing Engineering, Medical Laboratory Technology

View Paper: https://www.semanticscholar.org/paper/8ac68e4edebe0529307042124186f0c7ab59c15a

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In Industrial and Manufacturing Engineering

Citations in the corpus

Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing (2023, unknown): “Wang et al. [41] proposed a novel DT-based diagnostic framework for rotor unbalance fault of rotating machinery, in which a parameter sensitivity analysis strategy was developed to improve the model adaptability.”
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, methodology): “Another example of a digital state is in an industrial application of digital twin for smart manufacturing (Wang et al. 2019).”
A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives (2019, unknown): “Lu et al. (2019) proposed a DT approach for engine remanu-facturing suited for small scale operations while Wang and Wang (2019) developed DT product models to facilitate the recycling of electronic equipment.”
Digital twin paradigm: A systematic literature review (2021, methodology): “The digital twin has been developed also for the waste electrical and electronic equipment recovery to support the manufacturing/remanufacturing operations (Wang and Wang, 2018).”
A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics (2021, background): “Additionally, DT-based optimal control [126,127] as well as machine dynamics issues [128,129] from a rotating system [130] to feed drive [131,132] are other important aspects.”