Online Prognosis of Bimodal Crack Evolution for Fatigue Life Prediction of Composite Laminates Using Particle Filters

Abstract

Composite materials are extensively used in aircraft structures, wherein they are subjected to cyclic loads and subsequently impact-induced damages. Progressive fatigue degradation can lead to catastrophic failure. This highlights the need for an efficient prognostic framework to predict crack propagation in the field of structural health monitoring (SHM) of composite structures to improve functional safety and reliability. However, achieving good accuracy in crack growth prediction is challenging due to uncertainties in the material properties, loading conditions, and environmental factors. This paper presents a particle-filter-based online prognostic framework for damage prognosis of composite laminates due to crack-induced delamination and fiber breakage. An optimized Paris law model is used to describe the damage propagation in glass-fiber-reinforced polymer (GFRP) laminates subject to low-velocity impacts. Our proposed methodology deduces the jump energy/inflection point online wherein the damage growth switches from rapid degradation to slow degradation. The prediction results obtained are compared with the conventional Paris law model to validate the need for an optimized bimodal crack growth propagation model. The root mean square error (RMSE) and remaining useful life (RUL) prediction errors are used as the prognostic metrics.

Particle Filter Design for Robust Nonlinear Control System of Uncertain Heat Exchange Process with Sensor Noise and Communication Time Delay

“…P = ND −1 and P + ∆P = (N + ∆N)D −1 are the nominal plant and uncertain plant with right factorization, respectively, where N is stable, D is stable and invertible, and ∆N is unknown. Based on Bézout identity in Equation (8), if Equation (9) holds, where M and M are unimodular operators, then the nonlinear feedback control system with uncertainty is robust stable and the stabilizing operators S and R are designed as in Equation (10).…”

“…These methods can reduce the influence of noise or can act as an estimator to avoid the installation of real sensors, one of which is the particle filter (PF). The particle filter can estimate states, variance, and other parameters based on the measurement values with noise [10][11][12]. From the law of large numbers, the estimated value of the particle filter can be infinitely approximated to the real value when the number of particles is large enough.…”

A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model

“…For example, Zhou N et al [ 10 ] constructed the fatigue crack propagation model of the equipment and obtained the model parameters through experiments and simulation methods so as to simulate the crack propagation and RUL prediction of the two environments of the equipment. According to the physical characteristics of the product, Pugalenthi et al [ 11 ] proposed an online prognostic model based on the particle filter and constructed a prediction framework to predict the striation expansion for the life prediction of the product. Zou L et al [ 12 ] first proposed a fatigue life assessment method for press-fit axles that combines fretting wear, crack initiation, and propagation.…”

A Real-Time Remaining Fatigue Life Prediction Approach Based on a Hybrid Deep Learning Network

“…Eshwar et al [5] used an extended Kalman filter to jointly estimate the crack length and the parameters of the Paris' law of a structure, combining the effective stress intensity factor to predict the RFL online. Karkulali et al [6] proposed a particle filter algorithm to estimate the parameters of the Paris model for predicting the crack propagation and RFL of composite laminates. Generally, the approaches based on Paris' law rely on idealized conditions, and so the accuracy of prediction is affected.…”