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

Abstract

Plate-like structures are prone to the initiation of fatigue cracks during operation, primarily due to the harmonic loading imposed by the external environment. These micro-cracks possess the potential to progress into more severe forms, compromising structural integrity and, in extreme cases, leading to catastrophic accidents. The primary focus of this research is to introduce an online nonlinear framework for the early diagnosis and prognosis of fatigue cracks. This framework aims to detect nascent fatigue cracks in 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 signal, along with the S0 mode Lamb wave, is employed 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 (NOFRFs) are identified based on the established NARX model. An early fatigue crack damage index, denoted as optimal NOFRFs weighted contribution rate based on the KL divergence (KLRm), is extracted from the NOFRFs 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 aluminum lug joint specimens. The experimental results demonstrate the effectiveness and precision of the framework in estimating crack lengths and predicting RUL for nonlinear early fatigue cracks.