Inversion technique for quantitative infrared thermography evaluation of delamination defects in multilayered structures

Inverse analysis is a promising tool for quantitative evaluation offering informative model-based prediction and providing accurate reconstruction results without pre-inspections for characterization criteria. For traditional defect inverse reconstruction, a large number of parameters are required to reconstruct a complex defect, and the corresponding forward modelling simulation is very time-consuming. Such issues result in ill-posed and complex inverse reconstruction results, which further reduces its practical applicability. In this paper, we propose and experimentally validate an inversion technique for the reconstruction of complexly-shaped delamination defects in a multilayered metallic structure using signals derived from infrared thermography (IRT) testing. First, we employ a novel defect parameterization strategy based on Fourier series fitting to represent the profile of a complicated delamination defect with relatively few coefficients. Secondly, the multi-medium element modelling method is applied to enhance a FEM fast forward simulator, in order to solve the mismatching mesh issue for mesh updating during inversion. Thirdly, a deterministic inverse algorithm based on a penalty conjugate gradient algorithm is employed to realize a robust and efficient inverse analysis. By reconstructing delamination profiles with both numerically-simulated IRT signals and those obtained through laser IRT experiments, the validity, efficiency and robustness of the proposed inversion method are demonstrated for delamination defects in a double-layered plate. Based on this strategy, not only is the feasibility of the proposed method in Infrared thermography NDT validated, but the practical applicability of inversion reconstruction analysis is significantly improved

An efficient electromagnetic and thermal modelling of eddy current pulsed thermography for quantitative evaluation of blade fatigue cracks in heavy-duty gas turbines

The blade surface fatigue cracks often occur during service of Heavy-Duty Gas Turbines (HDGT) in high temperature, high rotational velocity and high frequency vibration environment. These fatigue cracks seriously threaten the safe operation of heavy-duty gas turbines, which would cause significant hazard or economic loss. The quantitative evaluation of blade surface fatigue cracks is extremely significant to HDGT. Eddy current pulsed thermography (ECPT) is an emerging non-destructive testing technology and show great potential for fatigue crack evaluation. This paper proposes a novel electromagnetic and thermal modelling of ECPT to achieve fast and effective quantitative evaluation for surface fatigue cracks. First, the proposed numerical method calculates electromagnetic field using the reduced magnetic vector potential method in the frequency domain based on frequency series method. The thermal source is transformed to an equivalent and simple form according to the energy equivalent method. Second, the temperature signals of ECPT are calculated through the time-domain iteration strategy with a relatively large time step. Then the ECPT experimental setup is established and the developed simulator is validated numerically and experimentally. The developed simulator is five times faster than the previous one and can be applied to eddy current thermography (ECT) with any kind of excitation waveforms. Finally, the depth of surface fatigue crack is quantitatively evaluated by means of the developed simulator, which is not only a promising simulation progress for ECPT, but also can be an effective tool embedded HDGT though-life maintenanc

Detectability evaluation of attributes anomaly for electronic components using pulsed thermography

Counterfeit Electronic Components (CECs) pose a serious threat to all intellectual properties and bring fatal failure to the key industrial systems. This paper initiates the exploration of the prospect of CEC detection using pulsed thermography (PT) by proposing a detectability evaluation method for material and structural anomalies in CECs. Firstly, a numerical Finite Element Modelling (FEM) simulation approach of CEC detection using PT was established to predict the thermal response of electronic components under the heat excitation. Then, by experimental validation, FEM simulates multiple models with attribute deviations in mould compound conductivity, mould compound volumetric heat capacity and die size respectively considering experimental noise. Secondly, based on principal components analysis (PCA), the gradients of the 1st and 2nd principal components are extracted and identified as two promising classification features of distinguishing the deviation models. Thirdly, a supervised machine learning-based method was applied to classify the features to identify the range of detectability. By defining the 90% of classification accuracy as the detectable threshold, the detectability ranges of deviation in three attributes have been quantitively evaluated respectively. The promising results suggest that PT can act as a concise, operable and cost-efficient tool for CECs screening which has the potential to be embedded in the initial large scale screening stage for anti-counterfeit