Rail Crack Detection Using Optimal Local Mean Decomposition and Cepstral Information Coefficient Based on Electromagnetic Acoustic Emission Technology

Chang, Yongqi; Zhang, Xin; Shen, Yi; Song, Shuzhi; Song, Qinghua; Cui, Jiazhong; Jie, Huamin; Zhao, Zhenyu

doi:10.1109/tim.2024.3375420

IEEE Trans. Instrum. Meas.

2024

DOI: 10.1109/tim.2024.3375420

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Rail Crack Detection Using Optimal Local Mean Decomposition and Cepstral Information Coefficient Based on Electromagnetic Acoustic Emission Technology

Yongqi Chang,

Xin Zhang,

Yi Shen

et al.

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2024

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Cited by 2 publications

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An Improved Target Network Model for Rail Surface Defect Detection

Zhang,

Feng,

Song

et al. 2024

Applied Sciences

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Rail surface defects typically serve as early indicators of railway malfunctions, which may compromise the quality and corrosion resistance of rails, thereby endangering the safe operation of trains. The timely detection of defects is essential to ensure the safe operation of railways. To improve the classification accuracy of rail surface defect detection, this paper proposes a rail surface defects detection algorithm based on MobileNet-YOLOv7. By integrating lightweight deep learning algorithms into the engineering application of rail surface defect detection, a MobileNetV3 lightweight network is used as the backbone network for YOLOv7 to enhance both speed and accuracy in complex defect extraction. Subsequently, the efficient intersection over union (EIOU) loss function is utilized as the positional loss function to bolster system resilience. Finally, the k-means++ clustering algorithm is applied to obtain new anchor boxes. The experimental results demonstrate the effectiveness of the proposed method, achieving superior detection accuracy compared with traditional algorithms.

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An Improved Target Network Model for Rail Surface Defect Detection

Zhang,

Feng,

Song

et al. 2024

Applied Sciences

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Prediction of Operational Noise Uncertainty in Automotive Micro-Motors Based on Multi-Branch Channel–Spatial Adaptive Weighting Strategy

Hu,

Deng,

Yan

et al. 2024

Electronics

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The acoustic performance of automotive micro-motors directly impacts the comfort and driving experience of both drivers and passengers. However, various motor production and testing uncertainties can lead to noise fluctuations during operation. Thus, predicting the operational noise range of motors on the production line in advance becomes crucial for timely adjustments to production parameters and process optimization. This paper introduces a prediction model based on a Multi-Branch Channel–Spatial Adaptive Weighting Strategy (MCSAWS). The model includes a multi-branch feature extraction (MFE) network and a channel–spatial attention module (CSAM). It uses the vibration and noise data from micro-motors’ idle operations on the production line as input to efficiently predict the operational noise uncertainty interval of automotive micro-motors. The model employs the VAE-GAN approach for data augmentation (DA) and uses Gammatone filters to emphasize the noise at the commutation frequency of the motor. The model was compared with Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs). Experimental results demonstrate that the MCSAWS method is superior to conventional methods in prediction accuracy and reliability, confirming the feasibility of the proposed approach. This research can help control noise uncertainty in micro-motors’ production and manufacturing processes in advance.

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Rail Crack Detection Using Optimal Local Mean Decomposition and Cepstral Information Coefficient Based on Electromagnetic Acoustic Emission Technology

Cited by 2 publications

References 38 publications

An Improved Target Network Model for Rail Surface Defect Detection

An Improved Target Network Model for Rail Surface Defect Detection

Prediction of Operational Noise Uncertainty in Automotive Micro-Motors Based on Multi-Branch Channel–Spatial Adaptive Weighting Strategy

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