An unknown fault identification method based on PSO-SVDD in the IoT environment

Xu, Erbao; Li, Yan; Peng, Lining; Yang, Mingshun; Liu, Yong

doi:10.1016/j.aej.2021.02.063

Cited by 17 publications

(4 citation statements)

References 9 publications

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“…First of all, in terms of the stability of power system operations, the traditional power system involves a relatively large area, so a small problem during the operation of the system may lead to power supply failure of the entire power system, resulting in large-scale power supply problems. In terms of system investment cost, a power system needs to pay a huge economic cost from the establishment of the system to the use and postmaintenance, and for such a huge project, postmaintenance is also very difficult; on the other hand, in some remote areas of the country with harsh natural environment, in order to operate the power system similarly, the need to invest more economic costs makes the final benefits even more unsatisfactory [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment

Zhao

Liu

2022

Journal of Environmental and Public Health

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In order to explore the current income gap, a method based on the PSO algorithm in the edge computing environment is proposed. PSO calculates and simulates bird flock foraging activities, the Frank Heppner biological group model, and the three rules in bird activities. After studying the activities of these natural creatures, abstract problems are quantified and similar models established. The Gini coefficient is calculated by using grouped data, and the grouping basis is also innovative. The quantile grouping method is adopted, which can effectively solve the difference between the concentration index and the Gini coefficient, and the Gini coefficients of each year can be added up to finally get the Gini coefficient of the stock income. Experimental results show that the Gini coefficient of traffic income in 2017 and 2018 had dropped significantly, but the variation of the Gini coefficient of stock income (Delta CG) was still greater than 0. Obviously, the adjustment speed of the Gini coefficient of stock income was lagging behind, as was the Gini coefficient of traffic income. We found that after 1986, the facilitation effect was greater than the dilution effect, and the facilitation effect continued to push up the stock income gap, which indicated more income flow to the high-income group, with the income flow gap showing an upward trend and the upward trend becoming more and more obvious. It has been proved that the PSO algorithm can effectively identify the income gap in the edge computing environment, and the corresponding policy suggestions are given.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment

Zhao

Liu

2022

Journal of Environmental and Public Health

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“…Xia et al proposed a fault diagnosis method based on SVDD for the irregularity of planetary gear fault characteristics of a helicopter main reducer [18]. In order to solve the problem of how to add unknown faults to the fault pattern library for adaptive updating of the fault diagnosis model when new unknown faults are identified, Xu et al proposed an unknown fault identification method based on PSO-SVDD [19]. The principle of SVDD follows below.…”

Section: Svddmentioning

confidence: 99%

Research on Performance Degradation Assessment and Abnormal Health Status Detection of Quayside Crane Lifting Gearbox Based on Information Fusion

Xiong

2023

JMSE

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In order to solve the problems of subjectivity in the extraction of traditional degradation features and incomplete degradation information contained in a single sensor signal, a performance degradation assessment and abnormal health status detection method based on information fusion for the quayside crane lifting gearbox is proposed. Firstly, the correlation between the vibration and temperature of the gearbox is analyzed; secondly, the Convolutional Neural Network (CNN) and entropy degradation features from the full fault cycle vibration and temperature data of the lifting gearbox are extracted respectively; then, the final degradation indicators of the vibration and temperature data are obtained, respectively, through feature optimization, and the fusion degradation indicator is obtained by combining the two indicators; finally, the performance degradation assessment and abnormal health status detection of the gearbox are carried out. The effectiveness and superiority of the proposed method in the performance degradation evaluation of the gearbox are verified by comparison, and the proposed method can identify the initial degradation point of the gearbox earlier than the method based on the single vibration degradation index and the method based on the fusion of the traditional vibration degradation feature and the temperature entropy degradation feature.

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“…Statistically, failures in essential electrical equipment, such as transformers and switches, are frequently characterized by abnormal heating phenomenon, including the corrosion of the switches, poor contact of the circuit breakers, deterioration, and moisture of potential transformers, etc. 4 . Prompt and accurate detection of abnormal temperatures is vital for assessing the operational status of electrical equipment, playing a crucial role in maintaining the safety and stability of substations 5 .…”

Section: Introductionmentioning

confidence: 99%

Thermal fault diagnosis of complex electrical equipment based on infrared image recognition

Tang,

Jian

2024

Sci Rep

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This paper realizes infrared image denoising, recognition, and semantic segmentation for complex electrical equipment and proposes a thermal fault diagnosis method that incorporates temperature differences. We introduce a deformable convolution module into the Denoising Convolutional Neural Network (DeDn-CNN) and propose an image denoising algorithm based on this improved network. By replacing Gaussian wrap-around filtering with anisotropic diffusion filtering, we suggest an image enhancement algorithm that employs Weighted Guided Filtering (WGF) with an enhanced Single-Scale Retinex (Ani-SSR) technique to prevent strong edge halos. Furthermore, we propose a refined detection algorithm for electrical equipment that builds upon an improved RetinaNet. This algorithm incorporates a rotating rectangular frame and an attention module, addressing the challenge of precise detection in scenarios where electrical equipment is densely arranged or tilted. We also introduce a thermal fault diagnosis approach that combines temperature differences with DeeplabV3 + semantic segmentation. The improved RetinaNet's recognition results are fed into the DeeplabV3 + model to further segment structures prone to thermal faults. The accuracy of component recognition in this paper achieved 87.23%, 86.54%, and 90.91%, with respective false alarm rates of 7.50%, 8.20%, and 7.89%. We propose a comprehensive method spanning from preprocessing through target recognition to thermal fault diagnosis for infrared images of complex electrical equipment, providing practical insights and robust solutions for future automation of electrical equipment inspections.

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An unknown fault identification method based on PSO-SVDD in the IoT environment

Cited by 17 publications

References 9 publications

[Retracted] Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment

[Retracted] Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment

Research on Performance Degradation Assessment and Abnormal Health Status Detection of Quayside Crane Lifting Gearbox Based on Information Fusion

Thermal fault diagnosis of complex electrical equipment based on infrared image recognition

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