Study on Magnetic Flux Leakage Detection of Broken Cable Wire Under Robot Spiral Climbing

Lu, Jialei; Song, Julong; Xu, Changyu; Fei, Xu

doi:10.23919/ccc55666.2022.9902860

Cited by 2 publications

(1 citation statement)

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“…Taking the defect width n as 5 mm and maintaining a separation distance of 5 mm, the defect depth h is simulated starting from 2 mm and increasing by 2 mm increments up to 8 mm, as shown in Figures 3 and 4. From the figures, it can be seen that, under the same conditions, changing the defect depth will change the peak-to-peak values of the axial and radial data [16].…”

Section: Two-dimensional Magnetic Leakage Signalmentioning

confidence: 99%

Improved Leakage Detection and Recognition Algorithm for Residual Neural Networks Based on Transfer Learning

Li,

Chen,

et al. 2023

Electronics

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Due to the lack of other component information in traditional magnetic leakage signal defects and the low accuracy of prediction methods, this paper proposes an improved residual network for magnetic leakage detection defect recognition method that predicts defect size and different detection speeds. A new defect diagnosis method based on ResNet18 on the Convolutional Neural Network (CNN) is proposed in this study. This method transfers the pre-trained ResNet18 network and replaces the activation function in the transferred network structure. It extracts features from transformed two-dimensional images obtained by converting the original experimental signals and signals with added noise, removing the influence of manual features. The results demonstrated that the improved ResNet18 network model, after transfer learning, achieved 100% prediction accuracy for all 10,000 grayscale images generated with defect lengths of 50 mm; width of 2 mm; and depths of 2 mm, 4 mm, 6 mm, and 8 mm. Moreover, the prediction accuracies for the quasi-static, slow, compensated fast, and fast scanning speeds were 99.20%, 98.50%, 93.30%, and 94.00%, respectively, for defect depths of 2 mm, 4 mm, 6 mm, and 8 mm. These accuracies surpass those of other models, demonstrating the significant improvement in prediction accuracy achieved by this method.

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Section: Two-dimensional Magnetic Leakage Signalmentioning

confidence: 99%