Xuefei Guan

An efficient analytical Bayesian method for reliability and system response updating based on Laplace and inverse first-order reliability computations

This paper presents an efficient analytical Bayesian method for reliability and system response updating without using simulations. The method includes additional information such as measurement data via Bayesian modeling to reduce estimation uncertainties. Laplace approximation method is used to evaluate Bayesian posterior distributions analytically. An efficient algorithm based on inverse first-order reliability method is developed to evaluate system responses given a reliability index or confidence interval. Since the proposed method involves no simulations such as Monte Carlo or Markov chain Monte Carlo simulations, the overall computational efficiency improves significantly, particularly for problems with complicated performance functions. A practical fatigue crack propagation problem with experimental data, and a structural scale example are presented for methodology demonstration. The accuracy and computational efficiency of the proposed method are compared with traditional simulation-based methods.

Probabilistic fatigue damage prognosis using maximum entropy approach

A general framework for probabilistic fatigue damage prognosis using maximum entropy concept is proposed and developed in this paper. The fatigue damage is calculated using a physics-based crack growth model. Due to the stochastic nature of fatigue crack propagation process, uncertainties arising from the underlying physical model, parameters of the model and the response variable measurement noise are considered and integrated into this framework. Incorporating all those uncertainties, a maximum relative entropy (MRE) approach is proposed to update the statistical description of model parameters and narrow down the prognosis deviations. A Markov Chain Monte Carlo (MCMC) simulation is then employed to generate samples from updated posterior probability distributions and provide statistical information for the maximum relative entropy updating procedure. A numerical toy problem is given to demonstrate the proposed MRE prognosis methodology. Experimental data for aluminum alloys are used to validate model predictions under uncertainty. Following this, a detailed comparison between the proposed MRE approach and the classical Bayesian updating method is performed to illustrate advantages of the proposed prognosis framework.

A multi-feature integration method for fatigue crack detection and crack length estimation in riveted lap joints using Lamb waves

This paper presents an experimental study of damage detection and quantification in riveted lap joints. Embedded lead zirconate titanate piezoelectric (PZT) ceramic wafer-type sensors are employed to perform in situ non-destructive evaluation (NDE) during fatigue cyclical loading. PZT wafers are used to monitor the wave reflection from the boundaries of the fatigue crack at the edge of bolt joints. The group velocity of the guided wave is calculated to select a proper time window in which the received signal contains the damage information. It is found that the fatigue crack lengths are correlated with three main features of the signal, i.e., correlation coefficient, amplitude change, and phase change. It was also observed that a single feature cannot be used to quantify the damage among different specimens since a considerable variability was observed in the response from different specimens. A multi-feature integration method based on a second-order multivariate regression analysis is proposed for the prediction of fatigue crack lengths using sensor measurements. The model parameters are obtained using training datasets from five specimens. The effectiveness of the proposed methodology is demonstrated using several lap joint specimens from different manufactures and under different loading conditions. (Some figures may appear in colour only in the online journal)

An Integrated Health Monitoring Method for Structural Fatigue Life Evaluation Using Limited Sensor Data

A general framework for structural fatigue life evaluation under fatigue cyclic loading using limited sensor data is proposed in this paper. First, limited sensor data are measured from various sensors which are preset on the complex structure. Then the strain data at remote spots are used to obtain the strain responses at critical spots by the strain/stress reconstruction method based on empirical mode decomposition (REMD method). All the computations in this paper are directly performed in the time domain. After the local stress responses at critical spots are determined, fatigue life evaluation can be performed for structural health management and risk assessment. Fatigue life evaluation using the reconstructed stresses from remote strain gauge measurement data is also demonstrated with detailed error analysis. Following this, the proposed methodology is demonstrated using a three-dimensional frame structure and a simplified airfoil structure. Finally, several conclusions and future work are drawn based on the proposed study.

A model assessment method for predicting structural fatigue life using Lamb waves

HighlightsModel assessment method for predicting structural fatigue life is proposed.POD models for Lamb wave‐based NDE are investigated.The influence of model choice on fatigue life prediction is studied. ABSTRACT This paper presents a study on model assessment for predicting structural fatigue life using Lamb waves. Lamb wave coupon testing is performed for model development. Three damage sensitive features, namely normalized energy, phase change, and correlation coefficient are extracted from Lamb wave data and are used to quantify the crack size. Four data‐driven models are proposed. The average relative error and the probability of detection (POD) are proposed as two measures to evaluate the performance of the four models. To study the influence of model choice on the probabilistic fatigue life prediction, probability density functions of the actual crack size are obtained from the POD models given the Lamb wave data. Crack growth model parameters are statistically identified using Bayesian parameter estimation with Markov chain Monte Carlo simulations. The model assessment and the influence of model choice on fatigue life prediction are made using both coupon testing data with artificial cracks and realistic lap joint testing data with naturally developed cracks.

A Fatigue Crack Size Evaluation Method Based on Lamb Wave Simulation and Limited Experimental Data

This paper presents a systematic and general method for Lamb wave-based crack size quantification using finite element simulations and Bayesian updating. The method consists of construction of a baseline quantification model using finite element simulation data and Bayesian updating with limited Lamb wave data from target structure. The baseline model correlates two proposed damage sensitive features, namely the normalized amplitude and phase change, with the crack length through a response surface model. The two damage sensitive features are extracted from the first received S0 mode wave package. The model parameters of the baseline model are estimated using finite element simulation data. To account for uncertainties from numerical modeling, geometry, material and manufacturing between the baseline model and the target model, Bayesian method is employed to update the baseline model with a few measurements acquired from the actual target structure. A rigorous validation is made using in-situ fatigue testing and Lamb wave data from coupon specimens and realistic lap-joint components. The effectiveness and accuracy of the proposed method is demonstrated under different loading and damage conditions.

Lamb Wave Damage Quantification Using GA-Based LS-SVM

Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE) for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM) and a genetic algorithm (GA). Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification.

Time Domain Strain/Stress Reconstruction Based on Empirical Mode Decomposition: Numerical Study and Experimental Validation.

Structural health monitoring has been studied by a number of researchers as well as various industries to keep up with the increasing demand for preventive maintenance routines. This work presents a novel method for reconstruct prompt, informed strain/stress responses at the hot spots of the structures based on strain measurements at remote locations. The structural responses measured from usage monitoring system at available locations are decomposed into modal responses using empirical mode decomposition. Transformation equations based on finite element modeling are derived to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. Then, two numerical examples (a two-span beam and a 19956-degree of freedom simplified airfoil) are used to demonstrate the overall reconstruction method. Finally, the present work investigates the effectiveness and accuracy of the method through a set of experiments conducted on an aluminium alloy cantilever beam commonly used in air vehicle and spacecraft. The experiments collect the vibration strain signals of the beam via optical fiber sensors. Reconstruction results are compared with theoretical solutions and a detailed error analysis is also provided.

Wavelet spectral finite element method for modeling wave propagation in stiffened composite laminates

Ultrasonic wave propagation in composite panels with stiffener is studied using wavelet spectral finite element (WSFE) method. WSFE is used to model dynamics of 2D finite composite panels with stiffeners accurately. Transverse shear flexibility is included in the model such that the accuracy holds up to wavelengths close to plate thickness. The global dynamic stiffness matrix of the structure is assembled following a procedure similar to regular finite element method and then solved in frequency-wavenumber domain. Wavelet transform is used to approximate the wave equations in time and one spatial dimension. WSFE results are validated with regular finite element method using Abaqus ® . The utility of 2D WSFE based models to analyze built-up composite structures is shown in the context of skin-stiffener debonding detection..

Time Domain Strain/Stress Reconstruction Based on Empirical Mode Decomposition: Numerical Study and Experimental Validation

Structural health monitoring has been studied by a number of researchers as well as various industries to keep up with the increasing demand for preventive maintenance routines. This work presents a novel method for reconstruct prompt, informed strain/stress responses at the hot spots of the structures based on strain measurements at remote locations. The structural responses measured from usage monitoring system at available locations are decomposed into modal responses using empirical mode decomposition. Transformation equations based on finite element modeling are derived to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. Then, two numerical examples (a two-span beam and a 19956-degree of freedom simplified airfoil) are used to demonstrate the overall reconstruction method. Finally, the present work investigates the effectiveness and accuracy of the method through a set of experiments conducted on an aluminium alloy cantilever beam commonly used in air vehicle and spacecraft. The experiments collect the vibration strain signals of the beam via optical fiber sensors. Reconstruction results are compared with theoretical solutions and a detailed error analysis is also provided.