| Abstract | Condition monitoring is essential for the industrial pipelines in manufacturing to ensure the consistent delivery of high quality products with efficient cost. Traditional pipeline conditional monitoring is driven by the entity in its physical space, with little connection to its virtual space. With the development of the digital twin, it is possible to implement the seamless convergence of physical and virtual space. To achieve this, the main challenges lie in building the high-fidelity digital twin, and keeping the connection and update between the physical pipeline and the digital twin. In this context, this paper presents a real-world case study of the pipeline condition monitoring towards digital twin system. This system comprises the components including individual physical pipeline, pipeline digital twin, pipeline knowledge library, Bayesian inference, and service station. Various key techniques (including sensing technique, finite element simulation, internet of things, advanced analytics, cloud computing and virtual reality) are integrated into the pipeline digital twin, to achieve its functionalities such as high-fidelity representation, probabilistic simulation, real-time update, health state monitoring, future state prediction and high-quality interaction. The damage detection, localization, quantification, and prediction are integrated as an ensemble considering uncertainty propagation. The developed pipeline digital twin is adopted to predict the reliability of a set of pipes suffered from fatigue cracking damage. Promising results show its potential for real-world application. |
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