Defect Depth Determination in Laser Infrared Thermography Based on LSTM-RNN

Abstract

Carbon fiber reinforced polymer (CFRP) has been increasingly used in aviation industry since it significantly enhances the performance of aircraft. However, imperfections inside the CFRP structures pose a threat to aviation safety. Apart from the defect shape and size, flaw depth is crucial to assess the defect severity. In this work, we utilize a laser infrared thermography (LIT) system to inspect an aviation CFRP sheet and adopt a long-short term memory recurrent neural network (LSTM-RNN) to determine the defect depth. Thermographic sequences obtained by LIT are processed using thermographic signal reconstructions (TSR) method. Raw data and TSR processed data are separately used to train and test the LSTM-RNN. Results show that background noises in the original thermal signals can be effectively reduced by the TSR method, which is helpful for the models to learn the signal characteristics. Compared with two traditional methods, recurrent neural network (RNN) and convolutional neural network (CNN), we find the LSTM-RNN outperforms.

Post-Disaster Emergency Communications Enhanced by Drones and Non-Orthogonal Multiple Access: Three-Dimensional Deployment Optimization and Spectrum Allocation

“…Specifically, the application of the RNN in solving the deployment optimization problem is primarily based on their powerful modeling capabilities for sequential data. The RNN can handle variable-length sequences and capture dependencies within the sequence through its recurrent structure, making it highly effective in solving convex optimization problems with continuous variables [27][28][29]. The RNN can gradually adjust parameters by learning temporal information, approaching the optimal solution in a stepwise manner and demonstrating strong optimization performance in convex optimization problems [30].…”

State of the Art in Defect Detection Based on Machine Vision

“…Hu et al [262] proposed an LSTM recurrent neural network (LSTM-RNN) model to classify common defects in an infrared thermography-based nondestructive testing task for honeycomb materials. Similarly, Wang et al [263] adopted the LSTM-RNN method to determine the defect depth inside carbon fiber reinforced polymer structures, achieving better performance than a CNN.…”

Deep learning techniques for non-destructive testing and evaluation

“…In particular, for order-1 tensor data are exploited when a limited amount of data is available. In [117,118,119], temperature time series signals are analyzed employing recurrent neural networks, where the temperature signatures were processed by employing a long-short memory recurrent neural network (LSTM-RNN) to classify different defects that typically affect carbon fiber reinforced plastic (CFRP) material in assembled structures. In [120,121], a datadrive approach has been used to train a 1D CNN architecture in order to perform pixel-wise pristine vs. damage classification of CFRP material based on temperature signature with respect to time.…”