Abstract
Eddy current testing technology is widely used in the defect detection of metal components and the integrity evaluation of critical components. However, at present, the evaluation and analysis of defect signals are still mostly based on artificial evaluation. Therefore, the evaluation of defects is often subjectively affected by human factors, which may lead to a lack in objectivity, accuracy, and reliability. In this paper, the feature extraction of non-linear signals is carried out. First, using the kernel-based principal component analysis (KPCA) algorithm. Secondly, based on the feature vectors of defects, the classification of an extreme learning machine (ELM) for different defects is studied. Compared with traditional classifiers, such as artificial neural network (ANN) and support vector machine (SVM), the accuracy and rapidity of ELM are more advantageous. Based on the accurate classification of defects, the linear least-squares fitting is used to further quantitatively evaluate the defects. Finally, the experimental results have verified the effectiveness of the proposed method, which involves automatic defect classification and quantitative analysis.
Highlights
Metal components composed of metal materials are widely used in the national defense industry, aerospace, petrochemical industry, rail transit, medical equipment, electronic information, and construction industries, playing an important role in a number of these industries [1,2]
The lift-off of the probe is fixed to 1 mm, and the scanning range in the horizontal direction is set to 10 mm
Considering the influence of the excitation frequency of the probe in the eddy current testing on the detection depth and sensitivity of the metal specimens, a low excitation frequency leads to a decrease of detection sensitivity, while a high excitation frequency leads to a decrease of detection depth [29]
Summary
Metal components composed of metal materials are widely used in the national defense industry, aerospace, petrochemical industry, rail transit, medical equipment, electronic information, and construction industries, playing an important role in a number of these industries [1,2]. In the production and use of metal components, it is easy to cause various defects and damage to the surface and interior of metal components. For some in-service metal component equipment, when the defects are serious, they lead to the scrapping of whole components, causing major safety problems [3,4]. Compared with other detection methods, eddy current testing has many advantages in detecting metal components. It can detect metal component defects [7] and other properties [8,9], especially on the surface and subsurface damage of metal components, such as cracks, folding, pore, and inclusions [10,11]
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