PHM-IRNET: Self-training thermal segmentation approach for thermographic inspection of industrial components

Abstract

Automating thermographic analysis for industrial and construction inspection is one of the essential aspects of NDT 4.0 in industrial transformation. Thermal segmentation is a preliminary step for characterizing a component’s heat signatures by detecting and extracting thermal patterns. Manual segmentation of areas with distinctive thermal patterns is a time-consuming task that consequently can increase the operation cost. Thus, automatic thermal segmentation can significantly assist companies in avoiding possible misinterpretation and decrease the operation cost. Due to the lack of enough labeled thermal images to train deep neural networks and the fact that, by nature, thermal abnormalities may have non-uniform patterns and form in different shapes and sizes, training a deep neural network can be a challenging task. Moreover, selecting common features can cause possible convergence onto dependency toward specific shapes or patterns. Thus, this paper proposes a self-training thermal segmentation method involving a novel process pipeline for using labeled and unlabeled data for training the presented deep-learning model. The method uses an unsupervised approach for predicting the ground truth in case of unlabeled data before passing the data to the secondary model for segmentation. For this paper, three experiments were conducted to evaluate the method’s performance and provide a comparative analysis with the most recent techniques. The first experiment used a simulated inspection created by COMSOL software. The second experiment employed several plate inspections in several isolated environments. Finally, the third experiment used thermal images acquired during an actual inspection of the piping structure and aerospace components.