Digital-twin-based structural diagnosis and prognosis are growing topics that have an important role in improving in-service safety and the economy. Current research focuses primarily on individual structures using Bayesian-based updating approaches, leaving little attention to the multiple similar structures at the fleet level. This study presents a novel copula-based approach for efficiently modeling multi-structure damage diagnosis and prognosis in a fleet. The proposed approach leverages the particle filter to model the damage growth in each structure and utilizes the copula function to capture the relationship of damage state between individuals as the joint probability distribution. The correlation parameters in the copula function are estimated based on the similarity of the predicted damage state and material parameters. Once an observation is available for a structure, the damage states of the structure and other structures in the fleet are updated via a copula-based updating step. The results from a hypothetical and an experiment dataset demonstrate that the proposed approach improves prediction accuracy compared to traditional individual-based methods and effectively controls uncertainties for each structure, even during intervals of no observations. This approach holds promise for integration into the fleet maintenance digital twin.
Copula-Based Collaborative Multistructure Damage Diagnosis and Prognosis for Fleet Maintenance Digital Twins
Zhou X.;Sbarufatti C.;Giglio M.;
2023-01-01
Abstract
Digital-twin-based structural diagnosis and prognosis are growing topics that have an important role in improving in-service safety and the economy. Current research focuses primarily on individual structures using Bayesian-based updating approaches, leaving little attention to the multiple similar structures at the fleet level. This study presents a novel copula-based approach for efficiently modeling multi-structure damage diagnosis and prognosis in a fleet. The proposed approach leverages the particle filter to model the damage growth in each structure and utilizes the copula function to capture the relationship of damage state between individuals as the joint probability distribution. The correlation parameters in the copula function are estimated based on the similarity of the predicted damage state and material parameters. Once an observation is available for a structure, the damage states of the structure and other structures in the fleet are updated via a copula-based updating step. The results from a hypothetical and an experiment dataset demonstrate that the proposed approach improves prediction accuracy compared to traditional individual-based methods and effectively controls uncertainties for each structure, even during intervals of no observations. This approach holds promise for integration into the fleet maintenance digital twin.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
