Digital twin-driven framework for fatigue life prediction of welded structures considering residual stress

Peng, Anyin; Ma, Yafei; Huang, Ke; Wang, Lei

doi:10.1016/j.ijfatigue.2024.108144

Cited by 10 publications

(2 citation statements)

References 48 publications

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“…Based on real-time interactions of DT between virtual models and physical structures, the degradation of a target system can be derived and evaluated effectively. DT is a way to organize and conduct RUL prediction bridges, and many studies show the efforts of DT frameworks applied in RUL prediction [27,28]. DT has favorable applications in RUL prediction involving vibration signals, such as rolling bearings [29,30] and other rotating machinery [31].…”

Section: Introductionmentioning

confidence: 99%

RUL Prediction for Piezoelectric Vibration Sensors Based on Digital-Twin and LSTM Network

Fu,

Gao,

Zhang

2024

Mathematics

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Piezoelectric vibration sensors (PVSs) are widely used in high-temperature environments, such as vibration measurements in aero-engines, because of their high accuracy, small size, and high temperature resistance. Accurate prediction of its RUL (Remaining Useful Life) is essential for applying and maintaining PVSs. Based on PVSs’ characteristics and main failure modes, this work combines the Digital-Twin (DT) and Long Short-Term Memory (LSTM) networks to predict the RUL of PVSs. In this framework, DT can provide rich data collection, analysis, and simulation capabilities, which have advantages in RUL prediction, and LSTM network has good results in predicting time sequence data. The proposed method exploits the advantages of those techniques in feature data collection, sample optimization, and RUL multiclassification. To verify the prediction of this method, a DT platform is established to conduct PVS degradation tests, which generates sample datasets, then the LSTM network is trained and validated. It has been proved that prediction accuracy is more than 99.7%, and training time is within 94 s. Based on this network, the RUL of PVSs is predicted using different test samples. The results show that the method performed well in prediction accuracy, sample data utilization, and compatibility.

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Section: Introductionmentioning

confidence: 99%

RUL Prediction for Piezoelectric Vibration Sensors Based on Digital-Twin and LSTM Network

Fu,

Gao,

Zhang

2024

Mathematics

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“…DT research has experienced rapid progress, especially in manufacturing, but defining the research areas remains a question because there is no unified definition [16]. To promote a base of knowledge shared among members and address implementation problems, further study is still required even though progress has been made in the realms of equipment health management, production control, optimization [17], and scheduling [18][19][20]. Nevertheless, there is still a noticeable gap, which is mostly defined by the lack of hands-on examples of real-live implementation.…”

mentioning

confidence: 99%

Scalable Compositional Digital Twin-Based Monitoring System for Production Management: Design and Development in an Experimental Open-Pit Mine

El Bazi,

Laayati,

Darkaoui

et al. 2024

Designs

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While digital twins (DTs) have recently gained prominence as a viable option for creating reliable asset representations, many existing frameworks and architectures in the literature involve the integration of different technologies and paradigms, including the Internet of Things (IoTs), data modeling, and machine learning (ML). This complexity requires the orchestration of these different technologies, often resulting in subsystems and composition frameworks that are difficult to seamlessly align. In this paper, we present a scalable compositional framework designed for the development of a DT-based production management system (PMS) with advanced production monitoring capabilities. The conducted approach used to design the compositional framework utilizes the Factory Design and Improvement (FDI) methodology. Furthermore, the validation of our proposed framework is illustrated through a case study conducted in a phosphate screening station within the context of the mining industry.

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Iterative updating of digital twin for equipment: Progress, challenges, and trends

Zhang,

Ding,

Zheng

et al. 2024

Advanced Engineering Informatics

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Digital twin-driven framework for fatigue life prediction of welded structures considering residual stress

Cited by 10 publications

References 48 publications

RUL Prediction for Piezoelectric Vibration Sensors Based on Digital-Twin and LSTM Network

RUL Prediction for Piezoelectric Vibration Sensors Based on Digital-Twin and LSTM Network

Scalable Compositional Digital Twin-Based Monitoring System for Production Management: Design and Development in an Experimental Open-Pit Mine

Iterative updating of digital twin for equipment: Progress, challenges, and trends

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