Research on the Signal Feature Extraction Method in Steel-Cord Conveyor Belt with Metal Magnetic Memory Testing

Qiao, Tiezhu; Lu, Xiaoyu Sean; Yan, Laiqing

doi:10.1166/asl.2012.2987

Cited by 3 publications

(3 citation statements)

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“…Step 1: First, φ =1 is fixed and a twofold increase is assumed in available resources, then a mini-grid search is performed according to Eq. (6)(7)(8)(9)(10)(11)(12)(13)(14) to finally find the three best parameters (𝛼, 𝛽, 𝛾) for EfficientNet-B0.…”

Section: Figure 8 Limitations Of Single Dimension Expansionmentioning

confidence: 99%

“…These methods need to be operated in the state of static offline, which makes it difficult to meet the actual detection requirements. There are various nondestructive testing (NDT) methods, such as tear sensors, eddy current detection [7], weak magnetic detection [8], X-ray detection [9], ECD (Embedded Conductive Detection) technology [10], etc. Most of them can meet the needs of realtime detection, but these methods still have problems such as low sensitivity, radiation to the human body, and high price.…”

Section: Introductionmentioning

confidence: 99%

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Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM

Yang,

Chen,

Liu

et al. 2024

IEEE Access

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The belt surface of the mine belt conveyor can cause serious wear under the condition of longterm high-load operation, which can have a negative impact on production, bring economic losses, even endanger personal safety and cause serious production accidents. Manual detection requires a lot of manpower and material resources, and is highly dependent on empirical judgment, which is with low efficiency and security risks. Therefore, in this study, we introduce a new conveyor belt wear detection algorithm Retinex EfficientNet-NAM-YOLOv8 (RENY algorithm) based on deep learning and machine vision technology to replace manual detection, improving detection efficiency and recognition accuracy. The wear degree of belt is reclassified and defined according to the mechanical properties and wear texture characteristics of belt with different wear degrees, and a new special data set for belt wear detection is established. Aiming at the low brightness, high noise and complex working conditions of the underground mine, Gaussian filtering and bilateral filtering are used as the central surround function of the improved Retinex algorithm, and then channel fusion is performed with the image after histogram equalization and adaptive brightness adjustment. The improved Retinex multi-image fusion algorithm is used to preprocess the collected image. EfficientNet has the performance of reasonably allocating the input resolution, network depth, and channel width, and is able to maximise the performance of the network with limited resources. EfficientNet is used to replace Darknet53 of YOLOv8 as the backbone of the feature extraction network, which improves the detection accuracy under limited computing resources. A lightweight attention module NAM is added to the improved network, which improves the detection speed without reducing the detection accuracy. Experimental results show that RENY algorithm effectively maintains the smoothness of the image during the image preprocessing stage, improves the brightness and contrast of the image, and better preserves the edge information of the image. RENY algorithm achieves a detection speed of 66FPS and an average accuracy of 98.57%. Compared with the original YOLOv8 algorithm, the accuracy of RENY algorithm is increased by 6.4% and the speed is increased by 13.2%. In comparison experiments with similar methods, RENY algorithm occupies less hardware resources, has strong generalization ability, good performance, and has high detection speed and accuracy.

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Section: Figure 8 Limitations Of Single Dimension Expansionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM

Yang,

Chen,

Liu

et al. 2024

IEEE Access

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show abstract

“…Further, if the damage of the conveyor belt cannot be detected and treated in time, it may eventually cause the occurrence of the conveyor belt tearing accident, which will cause greater harm. The current detection methods for conveyor belt tearing include: weak magnetic test [20], built-in sensor chip method [21], machine vision method [22][23][24][25], line laser-assisted method [26], infrared camera assisted method [27][28][29][30], audio-visual fusion method [31].Among them, most of the research work only focuses on the detection of tearing of conveyor belt, without the detection of other types of damage, which has a certain limitation.…”

Section: Introductionmentioning

confidence: 99%

Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt

Zhang

Zhou

et al. 2021

Applied Sciences

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Aiming at the problem that mining conveyor belts are easily damaged under severe working conditions, the paper proposed a deep learning-based conveyor belt damage detection method. To further explore the possibility of the application of lightweight CNNs in the detection of conveyor belt damage, the paper deeply integrates the MobileNet and Yolov4 network to achieve the lightweight of Yolov4, and performs a test on the exiting conveyor belt damage dataset containing 3000 images. The test results show that the lightweight network can effectively detect the damage of the conveyor belt, with the fastest test speed 70.26 FPS, and the highest test accuracy 93.22%. Compared with the original Yolov4, the accuracy increased by 3.5% with the speed increased by 188%. By comparing other existing detection methods, the strong generalization ability of the model is verified, which provides technical support and empirical reference for the visual monitoring and intelligent development of belt conveyors.

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A centernet-based direct detection method for mining conveyer belt damage

Zhang

Sun²,

Zhang³

et al. 2023

J Ambient Intell Human Comput

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Research on the Signal Feature Extraction Method in Steel-Cord Conveyor Belt with Metal Magnetic Memory Testing

Cited by 3 publications

References 0 publications

Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM

Wear State Detection of Conveyor Belt in Underground Mine Based on Retinex- YOLOv8-EfficientNet-NAM

Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt

A centernet-based direct detection method for mining conveyer belt damage

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