Tishun Peng

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)

A novel Bayesian imaging method for probabilistic delamination detection of composite materials

A probabilistic framework for location and size determination for delamination in carbon?carbon composites is proposed in this paper. A probability image of delaminated area using Lamb wave-based damage detection features is constructed with the Bayesian updating technique. First, the algorithm for the probabilistic delamination detection framework using the proposed Bayesian imaging method (BIM) is presented. Next, a fatigue testing setup for carbon?carbon composite coupons is described. The Lamb wave-based diagnostic signal is then interpreted and processed. Next, the obtained signal features are incorporated in the Bayesian imaging method for delamination size and location detection, as well as the corresponding uncertainty bounds prediction. The damage detection results using the proposed methodology are compared with x-ray images for verification and validation. Finally, some conclusions are drawn and suggestions made for future works based on the study presented in this paper.

Probabilistic fatigue damage prognosis of lap joint using Bayesian updating

A general framework for probabilistic prognosis and uncertainty management under fatigue cyclic loading is proposed in this article. First, the general idea using the Bayesian updating in prognosis is introduced. Several sources of uncertainties are discussed and included in the Bayesian updating framework. An equivalent stress level model is discussed for the mechanism-based fatigue crack growth analysis, which serves as the deterministic model for the lap joint fatigue life prognosis. Next, an in situ lap joint fatigue test with pre-installed piezoelectric sensors is designed and performed to collect experimental data. Signal processing techniques are used to extract damage features for crack length estimation. Following this, the proposed methodology is demonstrated using the experimental data under both constant and variable amplitude loadings. Finally, detailed discussion on validation metrics of the proposed prognosis algorithm is given. Several conclusions and future work are drawn based on the proposed study.

Integrated experimental and numerical investigation for fatigue damage diagnosis in composite plates

An integrated experimental and numerical investigation of fatigue damage diagnosis in composite plates is presented in this study. First, the fatigue testing setup for carbon–carbon composite coupons is described with corresponding health monitoring approach through Lamb wave–based diagnostic data collection. In order to study the effects of degradation evolution, a finite element model is used to simulate the effect on Lamb wave propagation due to fatigue-induced delamination and matrix cracking. Simulation results are compared with the experimental testing to first validate the model and then develop several features as potential damage indicators. A parametric study is conducted on the effects of varying degrees of delamination and matrix cracking on these features. Results from the model simulations are presented along with the data analysis and discussions on the capability and limitations of the approach. Finally, some conclusions are drawn and future work is proposed based on the results obtained so far.

Fatigue damage diagnosis and prognosis using Bayesian updating

In this paper, a methodology integrating a Lamb wave-based damage detection technique and a Bayesian updating method for remaining useful life (RUL) prediction is proposed. First, a piezoelectric sensor network is used to detect the fatigue crack size near the rivet holes in fuselage lap joints. Advanced signal processing and feature fusion is then used to quantitatively estimate the crack size. Second, a small time scale model is used as the physics model to predict the crack propagation for a given future loading and an estimate of initial crack length. Next, a Bayesian updating algorithm is implemented incorporating the damage diagnostic result and the small time scale model for RUL prediction. Probability distributions of model parameters are updated considering various uncertainties in the damage prognosis process. Finally, the proposed methodology is demonstrated using data from fatigue testing of realistic fuselage lap joints and the model predictions are validated using prognostics metrics.

Probabilistic fatigue life prediction of composite laminates using Bayesian updating

In this paper, the general framework for composite fatigue life prediction is proposed that can incorporate the detected stiffness from structural health monitoring system. First, an in-situ composite fatigue testing with pre-installed piezoelectric sensors is designed and performed to collected sensor signal and overall stiffness measurement. Signal processing techniques are implemented to extract damage identification features. Following this, power law is introduced to express the stiffness degradation mechanism and a general prognosis framework is proposed. Next, the detected overall stiffness degradation is integrated with a Bayesian inference framework for remaining useful life (RUL) prediction. The prognosis performance is validated using prognostics metric. Finally, some conclusions based this work are presented and directions for future work are drawn.

3D delamination profile reconstruction for composite laminates using inverse heat conduction

In this paper, a novel methodology for delamination profile diagnosis is proposed. First, the general inverse heat conduction problem (IHCP) is formulated and the corresponding adjoint problem derivation is presented. Following this, the conjugate gradient method with bound constraints is discussed and an one-dimensional numerical example is given to show the general procedures. Next, the heat conduction for two delaminated composite coupons is simulated using the commercial software ABAQUS. The extracted temperature history on the top and bottom surfaces is given as the available measurements in the delamination damage diagnosis framework. The detected results are compared with the ground truth to validate the feasibility of the proposed method. Finally, some conclusions and future work are drawn based on the current study.