Fatigue Crack Growth Diagnosis and Prognosis for Damage-Adaptive Operation of Mechanical Systems

Abstract

The digital twin paradigm that aims to integrate the information obtained from sensor data, physics models, operational data and inspection/maintenance/repair history of the system or component of interest, can potentially be used to optimize operational parameters that achieve a desired performance or reliability goal. In this paper, we discuss such a methodology for intelligent operation planning in mechanical systems. The proposed approach discusses two key components of the problem: damage diagnosis and damage prognosis. We consider the problem of diagnosis and prognosis of fatigue crack growth in a metal component, as an example. We discuss a probabilistic, Lamb-wave-scattering-based crack diagnosis framework that incorporates both aleatory and epistemic uncertainties in the diagnosis process. We build a Bayesian network for the Lamb-wave pitch-catch NDE using a low-fidelity physics-based model of the same. We perform global sensitivity analysis to quantify the contribution of various parameters to the variance of the damage-sensitive output signal feature(s) using this model. We retain the parameters with higher contribution, and build a medium-fidelity, one-way coupled, multi-physics model to simulate the piezoelectric effect and Lamb wave propagation. We perform Bayesian diagnosis of crack growth using the medium-fidelity model, considering data corrupted by measurement noise, and fuse the information from multiple sensors. We build a finite-element-based high-fidelity model for crack growth under uniaxial cyclic loading, and calibrate a phenomenological (low-fidelity) fatigue crack growth model using the high-fidelity model output. We use the resulting multi-fidelity model in a probabilistic crack growth prognosis framework; thus achieving both accuracy and computational efficiency. We integrate the probabilistic diagnosis and prognosis engines to estimate the damage state using both sensor data as well as model prediction.KeywordsInformation fusionLamb-wave pitch catchFatigue crack growthDigital twin