Image feature analysis for magnetic particle inspection of forging defects

Ye, Jhan-Hong; Ni, Rui-Hong; Hsu, Quang‐Cherng

doi:10.1177/09544054211014443

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…Magnetic particle flaw detection technology is commonly used in the crack detection of aviation products, trains, and ships. The magnetic powder sprayed on the ferromagnetic material will gather in the defective part, and then the cracks are identified using manual inspection or machine vision [ 9 ]. The method has high requirements for both the environment and the inspector’s skills.…”

Section: Introductionmentioning

confidence: 99%

A Novel Defect Detection Method for Overhead Ground Wire

Xiao,

Xiong,

Zhang

et al. 2023

Sensors

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Overhead ground wires typically have strong axial tension and are prone to structural defects caused by corrosion and lightning strikes, which could lead to serious safety hazards. Therefore, it is important to detect defects accurately and quickly to avoid those problems. Existing defect detection methods for overhead ground wires are mainly traditional metal defect detection methods, including eddy current detection, ultrasonic detection, and manual visual inspection. However, those methods have problems of low detection efficiency, high environmental requirements, and insufficient reliability. To solve the above problems, this paper studies a novel type of defect detection technology for overhead ground wire. Firstly, the magnetic leakage characteristics around the defects of overhead ground wires are analyzed, and the defect detection device is designed. Then, the influence of air gap, lift-off distance, defect width, and cross-sectional loss rate on the magnetic flux leakage signal is studied, a novel defect detection method for overhead ground wire is proposed, and experimental verification is carried out. The results show that the proposed method can accurately locate and quantify the defect, which has the advantages of good reliability and high efficiency and lays the foundation for preventing accidents caused by defective overhead ground wires.

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Section: Introductionmentioning

confidence: 99%

A Novel Defect Detection Method for Overhead Ground Wire

Xiao,

Xiong,

Zhang

et al. 2023

Sensors

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“…Therefore, timely detection of the health status of the rail surface is essential for preserving the security of the train. In traditional rail surface defect detection, the inspection methods are mostly ultrasonic [2], eddy current [3], and magnetic particle [4] methods. Although these methods can detect rail surface defects, they require much time.…”

Section: Introductionmentioning

confidence: 99%

Detection of Surface Defects on Railway Tracks Based on Deep Learning

Wang

Zhu

et al. 2022

IEEE Access

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The detection of rail surface defects is very important in railway transportation. However, the edge defects on both sides of the rail and the multi-scale variation between different types of defects both pose challenges to the detection of rail surface defects. In order to solve the above problems, this paper proposes a novel rail surface defect detection network, YOLOv5s-VF. First, we design a sharpening functional attention mechanism (V-CBAM) that contains two key components: adaptive channel attention (F-CAM) and sharpened spatial attention (SSA). In F-CAM, we use one-dimensional convolution with adaptive convolution kernels for cross-channel connections, which reduces the number of parameters of the attention mechanism without affecting its performance. In SSA, we design a sharpening filter suitable for spatial attention, which is used to enhance the attention to the edge position defects of railway tracks and enhance the detection effect of the network on edge defects. Second, we construct a microscale adaptive spatial feature fusion (M-ASFF), which adds a high-resolution feature extraction layer to enhance the details of the underlying features of tiny defects. At the same time, in order to prevent the loss of detailed information and the excessive increase of the parameters of the model, the low-resolution feature layer is removed. Combined with adaptive spatial feature fusion, it can prevent the semantic conflict caused by the fusion of features at different scales. Finally, given the lack of labeled public rail surface defect datasets, this paper is based on the collection of real rail images and manually labels defects to train an object detection network and open source it. The experimental results show that YOLOv5s-VF outperforms the existing rail surface defect detection methods with a detection accuracy of 93.5% and a detection speed of 114.9 fps.

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“…Visual inspection [4]- [6] or more modern techniques such as ultrasonic testing [7], [8], magnetic particle inspection [9], [10], liquid penetration testing [11], and eddy current testing [12], which are non-destructive testing (NDT), can be used to examine railway track faults.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis in Railway Track using Efficient Net based CNN

Mordia¹,

Verma²

2022

YMER

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Railway transportation is the most cost-effective and convenient means of passenger travel in India. The cress cross tracks are almost present in every part of India. Keeping in mind the security of people also the free running railway without any problem, we have to focus in the safety part of this system [1]. In India, the railway network accounts for over 80% of all transportation. Approximately 60% of accidents occur at crossings of railway tracks because of a railway track fracture, resulting in the loss of valuable life and economic damage. As a result, new technology is required for both fault detection and object detection in railway tracks. This technology must be resilient, efficient, and steady [2]. This paper presents a vision based method to find some common defects in railways. Some images have been collected of railway track and image processing method is used to preprocess these images and to detect the features related to defective parts. An EfficientNet based CNN model is developed to detect the defects which uses global average pooling, adam as optimizer, softmax as activation function and categorical_crossentropy as loss function. This research result consists of a classification report as defective and non-defective parts or image with accuracy of 91 percentages over 30 epochs. Keywords: Rail Track Images, Defects, Defect Detection Method, CNN and Defect Classification.

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Image feature analysis for magnetic particle inspection of forging defects

Cited by 7 publications

References 13 publications

A Novel Defect Detection Method for Overhead Ground Wire

A Novel Defect Detection Method for Overhead Ground Wire

Detection of Surface Defects on Railway Tracks Based on Deep Learning

Fault Diagnosis in Railway Track using Efficient Net based CNN

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