Permanently installed NDE sensors provide near-continuous integrity data that can be integrated into a digital twin framework for forecasting future fitness for service. The digital twin will need to integrate NDE data into an assumed probabilistic damage progression model, which incorporates uncertainty in a particular realisation of a component, its operating conditions, and the stochastic nature of damage progression. This study will focus on the example use-case of monitoring corrosion using local and/or long-range wall thickness data, but the methods presented will be generally applicable.The remaining useful life due to corrosion is assumed to be determined by a limiting minimum wall thickness. A stochastic pit growth model is used to project the possible growth trajectory of corrosion pits identified from an initial inspection. Order statistics and Monte Carlo simulations are then performed to estimate the likely maximum pit depth at a future point in time. Particle filtering methods are then used to integrate measurements obtained from permanently installed sensors to recursively update and improve the estimate in maximum pit depth as more data is collected.Two types of permanently installed sensors are considered in this study. First is the use of localised sensors with low area coverage but high sensitivity, such as point ultrasonic measurements. Second is the use of long-range sensors with large area-coverage but lower sensitivity, such as a guided wave measurement. Simulated examples are used to quantitatively compare the efficacy of using the two types of sensors for maximum pit depth estimation. The performance of the two monitoring approaches is also compared against the conventional approach of performing periodic manual inspections. The study provides a probabilistic methodology that can be used as the back end of a digital twin.

Probabilistic Integration of Structural Health Monitoring Data Into a Digital Twin for Structural Integrity Prognosis

Paper Proceedings: https://asmedigitalcollection.asme.org/qnde/QNDE2021/volume/85529

ASME 2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation (QNDE2021)

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