Electrical Equipment Identification Method With Synthetic Data Using Edge-Oriented Generative Adversarial Network

Niu, Zhewen; Reformat, Marek; Tang, Wenhu; Zhao, Baining

doi:10.1109/access.2020.3011689

Cited by 8 publications

(7 citation statements)

References 25 publications

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“…Various sensors in the electromechanical equipment system will generate voluminous data every day, and these data can hardly be mathematically modelled. However, based on the regularity between these process data, the multivariate statistical method can reduce their dimension, simplify these data, and analyse the data [15].…”

Section: Multivariate Statistical Methodsmentioning

confidence: 99%

“…The deviation fault is caused by the harsh environment of the sensor. As the sensor's service time prolongs, there will be a fixed deviation between the real value and the feedback value, as shown in equation (15). (15) Most draw-wire displacement sensors work under electromagnetic interference, low temperature, high humidity, or many pollutants.…”

Section: Fault Diagnosis Experiments Of Draw-wire Displacement Sensor Based On Pcamentioning

confidence: 99%

“…As the sensor's service time prolongs, there will be a fixed deviation between the real value and the feedback value, as shown in equation (15). (15) Most draw-wire displacement sensors work under electromagnetic interference, low temperature, high humidity, or many pollutants. Therefore, the failure rate is relatively high, with a lower average service life than general sensors.…”

Section: Fault Diagnosis Experiments Of Draw-wire Displacement Sensor Based On Pcamentioning

confidence: 99%

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Construction of an Electromechanical Automation and Fault Diagnosis System Using Data-Driven Technology in the Context of Big Data

2021

IJOMAM

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The sensor faults in electromechanical equipment are diagnosed and predicted to accelerate the industrial automation and stable operation of electromechanical equipment. Firstly, several basic fault prediction methods are introduced, and the data of electromechanical equipment during production can be well diagnosed and processed through data-driven technology. Secondly, the principal component analysis (PCA) method is introduced, and accordingly, the optimized dynamic principal component analysis (DPCA) method and several common sensor faults are proposed. Thirdly, the specific steps of the PCA and DPCA methods are discussed in sensor faults diagnosis, respectively. The PCA method can study the multivariate nonlinear data concurrently. On this basis, the optimized DPCA method can analyse the dynamic features of the real-time data collected by sensors. Finally, the fault type that produces a fixed deviation is studied. Then, the experiment of the draw-wire displacement sensor is simulated through the optimized PCA and DPCA. The square prediction error (SPE) and prediction-error rate are compared with the error. The results show that both methods can predict the fault of the sensor. The results of the T 2 statistics obtained through the PCA show that the sensor fault can be effectively predicted, but the prediction-error rate and fault-omission rate are also high. In the DPCA algorithm, the SPE statistics show that the DPCA algorithm can predict the fault with a small prediction-error rate and faultomission rate, which is far lower than the PCA algorithm. Therefore, dynamic factors analysis can improve sensor fault prediction, which is of great significance to the acceleration of industrial automation and equipment intelligence.

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Section: Multivariate Statistical Methodsmentioning

confidence: 99%

Section: Fault Diagnosis Experiments Of Draw-wire Displacement Sensor Based On Pcamentioning

confidence: 99%

Section: Fault Diagnosis Experiments Of Draw-wire Displacement Sensor Based On Pcamentioning

confidence: 99%

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Construction of an Electromechanical Automation and Fault Diagnosis System Using Data-Driven Technology in the Context of Big Data

2021

IJOMAM

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“…The detection of heating fault and discharge fault of electrical equipment is very important for the safe operation of power system [1][2][3]. In recent years, the operation and maintenance personnel have used spectral imagers and unmanned aerial vehicles to carry out intelligent patrol inspection on substation equipment, reducing the workload of substation operation and maintenance personnel in collecting and manual cutting, but the equipment identification and evaluation of multispectral images with big data characteristics still rely on manual methods, which has problems of low efficiency and high cost, and the identification of electrical equipment directly determines the effect of electrical equipment evaluation [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Electrical insulator identification method in multiple images based on two stage CNN network

Yi,

Li,

et al. 2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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Ultraviolet and infrared images are widely used to detect potential defects of electric equipment in advance. However, With the continuous expansion of the scale of electrical equipment, almost all the data is still uses manual analysis, which has some shortcoming example high cost. In order to improve the intelligence level of electric equipment detection, this paper proposes a multiple images identification method for electrical insulator based on two stage cnn network. First, a deep convolutional neural network identification model is built based on Faster-RCNN with initial weights in VGG. In addition, combine with the dataset expansion method example rotation, 462 images of electrical insulator are used for training. Finally, this model is used to identification image of equipment. The experimental show that the method proposed in this paper for electrical insulator map accuracy reaches 96.51%. Therefore, the proposed method can improve the identification accuracy, and provides condition for electrical equipment state detect.

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“…Basicamente redes GAN (Figura 12) consistem de duas redes, um gerador G e um discriminador D que competem um contra o outro [48]. Redes GAN têm sido utilizadas em diversos projetos.…”

Section: Diante Dessa Dificuldade Foram Propostas As Gans (Redes Gene...unclassified

Contribuições na inspeção de elementos da rede de distribuição de energia elétrica utilizando técnicas baseadas em deep learning

César Pereira

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Electrical Equipment Identification Method With Synthetic Data Using Edge-Oriented Generative Adversarial Network

Cited by 8 publications

References 25 publications

Construction of an Electromechanical Automation and Fault Diagnosis System Using Data-Driven Technology in the Context of Big Data

Construction of an Electromechanical Automation and Fault Diagnosis System Using Data-Driven Technology in the Context of Big Data

Electrical insulator identification method in multiple images based on two stage CNN network

Contribuições na inspeção de elementos da rede de distribuição de energia elétrica utilizando técnicas baseadas em deep learning

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