An Advanced Nonlinear Framework for Early Detection and Prognosis of Fatigue Cracks in Plate-Like Structures

The main objective of this research is to introduce an online nonlinear framework for the early detection and prognosis of fatigue cracks on plate-like structures, facilitating proactive measures to prevent structural failure and preserve the overall integrity. The initial phase of this study involves capturing Lamb waves generated by subjecting the inspected plate-like structures to an excitation signal. The excitation, along with the S0 mode Lamb wave, is used to identify a Nonlinear Autoregressive with eXogenous Inputs (NARX) model, serving as a representation of the underlying nonlinear dynamic mapping intrinsic to the inspected specimen. Subsequently, the Nonlinear Output Frequency Response Functions weighted contribution rate (WNOFRFs) is identified. An early fatigue crack damage index denoted as the optimal WNOFRFs based on the KL divergence (KLRm), is extracted and employed for estimating crack lengths. Additionally, the Particle Filter (PF) is utilized for predicting the Remain Useful Life (RUL). To validate the effectiveness of the proposed framework, fatigue experiments are conducted on an aluminum lug joint specimen. The results demonstrate the effectiveness and precision of the framework in estimating crack lengths and predicting RUL for early fatigue cracks of plate-like structures.