Ice accretion existence and three-dimensional shape identification based on infrared thermography detection

Abstract

Ice accretion detection is an important guarantee for production and life safety, especially for aircrafts. In addition, the existence and three-dimensional shape identification of ice accretion are essential to anti/de-icing technology. In this study, an ice accretion detection experiment based on flash infrared thermography system was conducted, followed by recognition approach for identifying the relationship between the infrared signal generates during infrared thermography detection and the shape of ice accretion. Firstly, data dimensionality reduction and mathematical modeling of infrared detection data were performed. Then, a new multi-task model for regression and classification, the multi-task Alexnet-CBAM (Convolutional block attention module) model, was developed through infrared detection of ice accretion samples and other objects. The multi-task Alexnet-CBAM model was exploited for ice thickness prediction as well. Further, the detected ice accretion was three-dimensionally reconstructed after object classification and thickness prediction on the basis of multi-task model regression. The results show that the accuracy of classification and thickness prediction of the multi-task Alexnet-CBAM model surpasses that of traditional convolutional networks. For different shapes of ice accretion, the classification accuracy of the model is higher than 99.91%, and the mean squared error of prediction thickness is less than 0.02. The proposed model is capable of reconstructing three-dimensional shape of ice accretion, which provides a new technology for accurate identification of ice accretion based on infrared thermography detection.