An advanced nonlinear framework for early detection and prognosis of fatigue cracks in plate-like structures

Plate-like structures are prone to the initiation of fatigue cracks during operation, primarily due to harmonic loading imposed by the external environment. These micro-cracks can progress and compromise structural integrity, potentially leading to catastrophic accidents. This research introduces an online nonlinear framework for the early detection and prognosis of fatigue cracks in plate-like structures, enabling proactive measures to prevent structural failure. 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 waves, is employed to identify a nonlinear autoregressive with exogenous input (NARX) model, serving as a representation of the underlying nonlinear dynamic mapping intrinsic to the inspected specimen. Subsequently, the improved Nonlinear Output Frequency Response Functions weighted contribution rate (WNOFRFs) is identified and an early fatigue crack damage index KLRm is extracted. Additionally, the Particle Filter (PF) is utilized for estimating crack lengths and predicting the Remain Useful Life (RUL). Fatigue experiments are conducted on aluminum lug joint specimens. The experimental results demonstrate the effectiveness and precision of the improved WNOFRFs in detecting early-stage fatigue crack growth. The integration of improved WNOFRFs with particle filtering (PF) outperforms existing methods in accuracy and reliability when assessing crack lengths and predicting the remaining useful life (RUL).