Threshold‐Optimized Swarm Decomposition Using Grey Wolf Optimizer for the Acoustic‐Based Internal Defect Detection of Arc Magnets

Huang, Qinyuan; Li, Qiang; Ran, Maoxia; Liu, Xin; Zhou, Ying

doi:10.1155/2021/6636873

Cited by 4 publications

(1 citation statement)

References 56 publications

(54 reference statements)

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“…It is a commonly used method. It is characterized by fast processing speed and simple operation and is a commonly used threshold selection method [15]. e basic idea is as follows:…”

Section: The Traditional Otsu Threshold Segmentation Algorithmmentioning

confidence: 99%

Spot Image Segmentation of Lifting Container Vibration Based on Improved Threshold Method and Mathematical Morphology

et al. 2021

Shock and Vibration

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Because the mine is damp and dark, it is not easy to detect the rigid tank channel’s structural failure directly. Therefore, we judged the tank channel’s surface condition by detecting the magnitude of the vibration displacement of the lifting container. In our study, we used a laser vision system to measure the structural vibration displacement. In order to accurately segment the laser spot information from the vibration image, we proposed an approach that links the relationship between the gray value of the area adjacent to the threshold point and the background’s gray value to the target in the image. We used MCE to evaluate the segmentation effect of threshold segmentation and verified the improved algorithm’s accuracy by detecting the pixel centroid of laser spots. Results show that the improved algorithm in our study has the best threshold segmentation effect, the error classification can be close to 0.0003, and the minimum deviation of the obtained vibration displacement is close to 0.1 pixels, which can realize the accurate extraction of the vibration signal of the vertical shaft tank. The novelty of this method lies in the accurate threshold segmentation and noise reduction processing of the laser speck vibration image under various interference environments in the operation of the mine hoisting system and the accurate acquisition of vibration signals. The research work provides a basis for the accurate evaluation of mechanical faults of automation technology.

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“…It is a commonly used method. It is characterized by fast processing speed and simple operation and is a commonly used threshold selection method [15]. e basic idea is as follows:…”

Section: The Traditional Otsu Threshold Segmentation Algorithmmentioning

confidence: 99%

Spot Image Segmentation of Lifting Container Vibration Based on Improved Threshold Method and Mathematical Morphology

et al. 2021

Shock and Vibration

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A parameter-optimized variational mode decomposition method using salp swarm algorithm and its application to acoustic-based detection for internal defects of arc magnets

Huang

Liu

et al. 2021

AIP Advances

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The acoustic-based detection is regarded as an effective way to detect the internal defects of arc magnets. Variational mode decomposition (VMD) has a significant potential to provide a favorable acoustic signal analysis for such detection. However, the performance of VMD heavily depends on the proper parameter setting. The existing optimization methods for determining the optimal VMD parameter setting still expose shortcomings, including slow convergences, excessive iterations, and local optimum traps. Therefore, a parameter-optimized VMD method using the salp swarm algorithm (SSA) is proposed. In this method, the relationship between the VMD parameters and their decomposition performance is quantified as a fitness function, the minimum value of which indicates the optimal parameter setting. SSA is used to search for such a minimum value from the parameter space. With the optimized parameters, each signal can be decomposed accurately into a series of modes representing signal components. The center frequencies are extracted from the selected modes as feature data, and their identification is performed by random forest. The experimental results demonstrated that the detection accuracy is above 98%. The proposed method has superior performance in the VMD parameter optimization as well as the acoustic-based internal defect detection of arc magnets.

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Adaptive Swarm Decomposition Guided by Spectral Characteristic Information Scanner and Its Application for Bearing Fault Diagnosis

Song

Jiang

Liu

et al. 2022

IEEE Trans. Instrum. Meas.

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Threshold‐Optimized Swarm Decomposition Using Grey Wolf Optimizer for the Acoustic‐Based Internal Defect Detection of Arc Magnets

Cited by 4 publications

References 56 publications

Spot Image Segmentation of Lifting Container Vibration Based on Improved Threshold Method and Mathematical Morphology

Spot Image Segmentation of Lifting Container Vibration Based on Improved Threshold Method and Mathematical Morphology

A parameter-optimized variational mode decomposition method using salp swarm algorithm and its application to acoustic-based detection for internal defects of arc magnets

Adaptive Swarm Decomposition Guided by Spectral Characteristic Information Scanner and Its Application for Bearing Fault Diagnosis

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