Convolutional Neural Networks for Electrical Endurance Prediction of Alternating Current Contactors

Cui, Hechen; Wu, Ziran; Wu, Guichu; Xu, Xiaofeng; You, Yingmin; Yan-dong, Fang

doi:10.1109/tcpmt.2019.2930741

Cited by 12 publications

(8 citation statements)

References 21 publications

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“…The initial study [17] applied conditional density estimation for residual electrical endurance prediction for AC contactors, based on the investigation of the relation between electrical endurance and breaking arc characteristics. A later paper [18] firstly proposed to map the break operation signals of AC contactors to several stages of contact mass loss by deep learning classification. Another conference paper [19] preliminarily discussed the feasibility of using CNNR for continuous contact mass loss estimation.…”

Section: B Related Work Of the Research Groupmentioning

confidence: 99%

“…2). The same as described in paper [18], the corresponding ACML value of an operation is achieved by a piecewise linear interpolation (1) of mass measurements after every 600 operations. M i represents the corresponding ACML of the ith operation, j is the count of the latest mass-measured operation before the ith operation, and k is the count of the next mass-measured operation after the ith operation.…”

Section: B Data Acquisition and Pre-processingmentioning

confidence: 99%

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A CNN-Regression-Based Contact Erosion Measurement Method for AC Contactors

Fang²,

Wu³

et al. 2022

IEEE Trans. Instrum. Meas.

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This paper proposes a method that indirectly measures the contact erosion of alternating current (AC) contactors via mapping electrical signals to the contacting alloy erosion condition which is represented by the accumulated contact mass loss (ACML). Electrical signal waveforms and their corresponding ACMLs of every make-and-break operation are acquired in endurance tests. A supervised convolutional neural network regression (CNNR) architecture containing six onedimensional convolution layers is proposed to model the relation between waveforms and ACMLs. We compare different CNNR architectures as well as different training schemes by the test precision to obtain the optimal solution. Experiments prove that the proposed CNNR architecture with an optimized training scheme can achieve a precise ACML measurement when only voltage waveforms of make operations are used. The best results reach mean absolute errors of 3.29% and 1.59% corresponding to two datasets respectively, which are superior to the results of other regression methods in the comparison and prove the theoretical significance and applicational values.

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Section: B Related Work Of the Research Groupmentioning

confidence: 99%

Section: B Data Acquisition and Pre-processingmentioning

confidence: 99%

A CNN-Regression-Based Contact Erosion Measurement Method for AC Contactors

Fang²,

Wu³

et al. 2022

IEEE Trans. Instrum. Meas.

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“…This paper uses a deconvolution network to perform unsupervised feature extraction on the original input image. A deconvolution network is a neural network consisting of alternating deconvolution layers, depooling layers, and correction layers [35]. The layered network structure helps to extract the middle and high-level features of the image.…”

Section: Feature Extraction Model Based On Deconvolutionmentioning

confidence: 99%

“…Bai et al [34] proposed a unified integrated diffusion deep learning framework that can improve the pedestrian recognition effect by imposing additional constraints on the objective function and changing the solver for similarity propagation. Cui et al [35] proposed a greedy hierarchical unsupervised learning algorithm, which is a generative model causal variable with many hidden layers. It can provide ideas for the training of unsupervised deep learning pedestrian recognition models.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Reidentification Algorithm Based on Deconvolution Network Feature Extraction‐Multilayer Attention Mechanism Convolutional Neural Network

Liu

Lei

2021

Journal of Sensors

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Pedestrian reidentification is a key technology in large-scale distributed camera systems. It can quickly and efficiently detect and track target people in large-scale distributed surveillance networks. The existing traditional pedestrian reidentification methods have problems such as low recognition accuracy, low calculation efficiency, and weak adaptive ability. Pedestrian reidentification algorithms based on deep learning have been widely used in the field of pedestrian reidentification due to their strong adaptive ability and high recognition accuracy. However, the pedestrian recognition method based on deep learning has the following problems: first, during the learning process of the deep learning model, the initial value of the convolution kernel is usually randomly assigned, which makes the model learning process easily fall into a local optimum. The second is that the model parameter learning method based on the gradient descent method exhibits gradient dispersion. The third is that the information transfer of pedestrian reidentification sequence images is not considered. In view of these issues, this paper first examines the feature map matrix from the original image through a deconvolution neural network, uses it as a convolution kernel, and then performs layer-by-layer convolution and pooling operations. Then, the second derivative information of the error function is directly obtained without calculating the Hessian matrix, and the momentum coefficient is used to improve the convergence of the backpropagation, thereby suppressing the gradient dispersion phenomenon. At the same time, to solve the problem of information transfer of pedestrian reidentification sequence images, this paper proposes a memory network model based on a multilayer attention mechanism, which uses the network to effectively store image visual information and pedestrian behavior information, respectively. It can solve the problem of information transmission. Based on the above ideas, this paper proposes a pedestrian reidentification algorithm based on deconvolution network feature extraction-multilayer attention mechanism convolutional neural network. Experiments are performed on the related data sets using this algorithm and other major popular human reidentification algorithms. The results show that the pedestrian reidentification method proposed in this paper not only has strong adaptive ability but also has significantly improved average recognition accuracy and rank-1 matching rate compared with other mainstream methods.

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“…Electrical contact endurance of silver coating was defined by a modified Archard law in [17]. Machine learning technology has also attracted attention on the electrical endurance and failure prediction of electrical connectors [18]. Reliability of electrical contact could strongly affect the safe operation of GIB equipment.…”

Section: Introductionmentioning

confidence: 99%

Deterioration Behavior Analysis and LSTM-Based Failure Prediction of GIB Electrical Contact Inside Various Insulation Gases

et al. 2020

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Plug-in connector of gas insulated bus (GIB) could be subject to the wear and electrical contact resistance (ECR) deterioration processes under cyclic mechanical and current loads. A specific test platform is designed to simulate the current-carrying wear and ECR degradation process of Ag-plated GIB electrical contact spot inside SF6, N2 and Air insulation mediums under current loads. Worn surface morphology and chemical composition are observed and analyzed. Influence of insulation medium and current load on deterioration mechanism of GIB electrical contact are discussed. Raw ECR curve obtained from current-carrying experiment is smoothed by the exponentially weighted moving average (EWMA) method. A long short-term memory (LSTM) neural network is then proposed to realize both single-step and multi-step ECR and failure predictions of GIB electrical contact. Results show that adhesive wear on contact surface happened due to materials transfer. Deterioration of contact spot inside air medium is much severer than those in SF6 and N2 due to oxidation effect. Deterioration of contact spot inside SF6 medium under high current load is severer than those under low current load due to the change of wear behavior and chemical reactions. Compared with other methods, LSTM could achieve better performance in both singlestep and multi-step failure prediction of GIB electrical contact. INDEX TERMS electrical contact resistance (ECR); gas insulated bus (GIB); insulation gases; LSTM.

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Convolutional Neural Networks for Electrical Endurance Prediction of Alternating Current Contactors

Cited by 12 publications

References 21 publications

A CNN-Regression-Based Contact Erosion Measurement Method for AC Contactors

A CNN-Regression-Based Contact Erosion Measurement Method for AC Contactors

Pedestrian Reidentification Algorithm Based on Deconvolution Network Feature Extraction‐Multilayer Attention Mechanism Convolutional Neural Network

Deterioration Behavior Analysis and LSTM-Based Failure Prediction of GIB Electrical Contact Inside Various Insulation Gases

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