A quantitative identification method based on CWT and CNN for external and inner broken wires of steel wire ropes

Zhang, Yiqing; Feng, Zesen; Sheng, Shi; Dong, Zhihu; Zhao, Ling; Jing, Lu; Tan, Jun

doi:10.1016/j.heliyon.2022.e11623

Cited by 11 publications

(3 citation statements)

References 29 publications

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“…Deep learning approaches based on convolutional neural networks (CNN) and CWT which characterize the AE signal as a twodimensional time-frequency image are currently used in real-time material characterization and classification (Adeniji et al, 2022;Barbosh et al, 2022a;Barile et al, 2022;Zhen et al, 2019). The combination of CNN and CWT has led to significant success in various research studies (Gu et al, 2022;Manganelli Conforti et al, 2022;Reddy et al, 2021;Vetova, 2021;Zhang et al, 2022). While ordinary CNNs have shown significant performance, there is still room for improvement (Alzubaidi et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Enhancing the acoustic emission technique using fuzzy artificial bee colony-based deep learning for characterizing selective laser melted AlSi10Mg specimens

Barile,

Casavola,

Katamba Mpoyi

et al. 2024

International Journal of Damage Mechanics

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This article presents a classification of Acoustic Emission (AE) signals from AlSi10Mg specimens produced via Selective Laser Melting (SLM). Tensile tests characterized the mechanical properties of specimens printed in different orientations (X, Y, Z, 45°). Initially, a study quantified damage modes based on the stress-strain curve and cumulative AE energy. AE signals for each specimen (X, Y, 45°, Z), across deformation stages (elastic and plastic), and damage modes were analyzed using continuous wavelet transform to extract time-frequency features. A novel convolutional neural network, based on artificial bee colonies and fuzzy C-means, was developed for scalogram classification. Data augmentation with Gaussian white noise enhanced the approach. Cross-validation ensured robustness against overfitting and suboptimal local maxima. Evaluation metrics, including the confusion matrix, precision-recall curve, and F1 score, demonstrated the algorithm's high accuracy of 92.6%, precision-recall curve of 92.5%, and F1 score of 92.5% for AE signals based on printing direction (X, Y, 45°, Z). The study highlighted the potential for improving AE signal classification related to elastic and plastic deformation stages with 100% accuracy. For damage modes, the algorithm achieved a confusion matrix accuracy of 90.6%, a precision-recall curve of 90.4%, and an F1 score of 90.5%. This approach demonstrates high accuracy in classifying AE signals across different printing orientations, deformation stages, and damage modes of AlSi10Mg specimens manufactured through SLM.

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Section: Introductionmentioning

confidence: 99%

Enhancing the acoustic emission technique using fuzzy artificial bee colony-based deep learning for characterizing selective laser melted AlSi10Mg specimens

Barile,

Casavola,

Katamba Mpoyi

et al. 2024

International Journal of Damage Mechanics

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“…At the same time, the combination of the denoising method and machine learning provides a new direction for wire rope damage identi cation. Zhang et al conducted wavelet denoising for the MFL signal of the wire rope [15]. Then they conducted damage classi cation and feature extraction in space, and nally input the features into a wavelet neural network for damage identi cation.…”

Section: Introductionmentioning

confidence: 99%

Damage identification of wire rope under noise background via Light- EfficientNetV2 and Magnetic flux leakage image

Huang

Wang

Yang

et al. 2023

Preprint

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Magnetic flux leakage (MFL) testing, non-destructive testing, can prevent some major accidents of hoist equipment by identifying the damage of wire ropes. However, in harsh working conditions such as mines and oil wells, the inevitable vibration and swing of wire rope will generate noise and interfere with the MFL signal, which makes us difficult to identify the damage. As a classification network, Convolutional neural network (CNN) is positive in recognition accuracy and noise resistance, but it hardly uses in wire rope damage classification. To improve the accuracy of wire rope damage identification under noise background, we propose a method of wire rope damage identification via Light-EfficientNetV2 and MFL image. First, the MFL signal is segmented and rearranged to form the MFL image, and then the image is classified by Light-EfficientNetV2. To improve the classification efficiency, we reduce the layers of EfficientNetV2 to make it lighter. Finally, the availability of this method is proved by the validation set. Compared with four neural networks, the accuracy is the highest. Moreover, as the noise increased, the accuracy of Light-EfficientNetV2 is higher than EfficientNetV2, which has application value in the wire rope damage identification under noise background.

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“…In recent years, breakthroughs in image processing methods have provided increased directions for wire rope signal processing and feature extraction techniques. Based on the matrix reconstruction method and the fact that two-dimensional (2D) imaging provides a better platform for feature extraction in comparison to standard one-dimensional (1D) signals, numerous detection algorithms have been proposed and demonstrated in the literature [6][7][8]. To improve the robustness and accuracy outcomes of wire rope defect inspections, Liu et al proposed some novel methods to complete quantitative defect recognition practices, including a reshaped sine function, wavelet function, and grid entropy.…”

Section: Introductionmentioning

confidence: 99%

A New Defect Diagnosis Method for Wire Rope Based on CNN-Transformer and Transfer Learning

Wang

Xue

2023

Applied Sciences

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Accurate wire rope defect diagnosis is crucial for the health of whole machinery systems in various industries and practical applications. Although the loss of metallic cross-sectional area signals is the most widely used method in non-destructive wire rope evaluation methods, the weakness and scarcity of defect signals lead to poor diagnostic performance, especially in diverse conditions or those with noise interference. Thus, a new wire rope defect diagnosis method is proposed in this study. First, empirical mode decomposition and isolation forest methods are applied to eliminate noise signals and to locate the defects. Second, a convolution neural network and transformer encoder are used to design a new wire rope defect diagnosis network for the improvement of the feature extraction ability. Third, transfer learning architecture is established based on gray feature images to fine-tune the pre-trained model using a small target domain dataset. Finally, comparison experiments and a visualization analysis are conducted to verify the effectiveness of the proposed methods. The results demonstrate that the presented model can improve the performance of the wire rope defect diagnosis method under cross-domain conditions. Additionally, the transfer feasibility of transfer learning architecture is discussed for future practical applications.

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A quantitative identification method based on CWT and CNN for external and inner broken wires of steel wire ropes

Cited by 11 publications

References 29 publications

Enhancing the acoustic emission technique using fuzzy artificial bee colony-based deep learning for characterizing selective laser melted AlSi10Mg specimens

Enhancing the acoustic emission technique using fuzzy artificial bee colony-based deep learning for characterizing selective laser melted AlSi10Mg specimens

Damage identification of wire rope under noise background via Light- EfficientNetV2 and Magnetic flux leakage image

A New Defect Diagnosis Method for Wire Rope Based on CNN-Transformer and Transfer Learning

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