A novel machine learning model for eddy current testing with uncertainty. (January 2019)

• Record Type: Journal Article
• Authors: Zhu, Peipei; Cheng, Yuhua; Banerjee, Portia; Tamburrino, Antonello; Deng, Yiming
• Is Part Of: NDT & E international. Volume 101(2019)
• Page Start: 104
• Page End: 112
• Publication Date: 2019-01
• Subjects: Convolutional neural network -- Eddy current testing -- Uncertainty quantification -- Data classification; Nondestructive testing -- Periodicals; Contrôle non destructif -- Périodiques; Electronic journals; 620.1127
• DOI: 10.1016/j.ndteint.2018.09.010
• Languages: English
• ISSNs: 0963-8695

Abstract

Abstract: A novel deep learning based eddy current inversion algorithm is proposed and investigated in this paper. Eddy current testing (ECT) for defects detection problem is adopted to demonstrated the proposed algorithms. The proposed model based on a Convolutional Neural Network (CNN) is developed to improve the defect detection performance with uncertainty information. The novelty of our work consists in combining characteristics of ECT data with general deep learning model to improve performance of deep learning in ECT field including a region of interest (ROI) method based on robust principle component analysis, a CNN classification model with weighted loss function and measurement of uncertainties. Experimental dataset obtained from eddy current inspection of heat exchanger tubes is utilized to validate the detection performance improvement. As a result, both the classification accuracy and the percentage of defects correctly identified have been increased to almost 100%.