Real-time Deep Neural Networks for internet-enabled arc-fault detection

Siegel, Joshua E.; Pratt, Shane; Sun, Yongbin; Sarma, Sanjay E.

doi:10.1016/j.engappai.2018.05.009

Cited by 77 publications

(39 citation statements)

References 8 publications

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“…Continuing research into AI-TSR has the potential to enable bespoke manufacturing and late differentiation for AM (FFF or otherwise) in critical industries, while the use of features in excess of polynomial coefficients (for example MFCC, DWT, DFT, or kurtosis as used in [28], [27]) offer avenues for accuracy improvement, and may provide sufficient granularity to extend the DNN to also measure delamination depth as well as thickness. In the future, we will print additional samples and extract TSR features to determine the limit of the classifier's sensitivity with regards to feature depth and delamination thickness, or to identify the minimum identifiable defect diameter and height in the case of defects such as voids.…”

Section: Discussionmentioning

confidence: 99%

“…Probable fault size or defect shape may be determined through pixel clustering, reporting the coordinates of sufficiently large faults to secondary human inspectors. Building this system into a mobile service [29] or for embedded hardware [27] could further improve in-the-field utility, allows the classifier's use where conventional NDT imaging solutions are infeasible or where inspection latency and turnaround time is a critical concern.…”

Section: Discussionmentioning

confidence: 99%

“…We split these feature vectors into training (80%) and testing (20%) sets at random and without repetition. These data were fed into a DNN with three layers possessing 16, 32 and 16 fully connected units, similar to the DNN described in [27]. Our learning rate was initially 1e−7, with a decay step of 1, 000 and a decay rate of 0.9.…”

Section: Proof Of Concept (Synthetic Data)mentioning

confidence: 99%

See 2 more Smart Citations

Automated non-destructive inspection of Fused Filament Fabrication components using Thermographic Signal Reconstruction

Siegel

Beemer

Shepard

2020

Additive Manufacturing

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Manufacturers struggle to produce low-cost, robust and complex components at manufacturing lot-size one. Additive processes like Fused Filament Fabrication (FFF) inexpensively produce complex geometries, but defects limit viability in critical applications. We present an approach to high-accuracy, high-throughput and low-cost automated non-destructive testing (NDT) for FFF interlayer delamination using Flash Thermography (FT) data processed with Thermographic Signal Reconstruction (TSR) and Artificial Intelligence (AI). A Deep Neural Network (DNN) attains 95.4% per-pixel accuracy when differentiating four delamination thicknesses 5mm subsurface in PolyLactic Acid (PLA) widgets, and 98.6% accuracy in differentiating acceptable from unacceptable condition for the same components. Automated inspection enables time-and costefficient 100% inspection for delamination defects, supporting FFF's use in critical and small-batch applications.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Proof Of Concept (Synthetic Data)mentioning

confidence: 99%

See 1 more Smart Citation

Automated non-destructive inspection of Fused Filament Fabrication components using Thermographic Signal Reconstruction

Siegel

Beemer

Shepard

2020

Additive Manufacturing

Self Cite

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“…Gulsah Karaduman et al [18] presented a deep learning approach using convolutional neural network to detect arc faults in pantograph-catenary system. Joshua E. Siegel et al [19] developed a deep neural network taking Fourier coefficients, Mel-Frequency Cepstrum data, and Wavelet features as input for detecting and disrupting electronic arc faults. Qiongfang Yu et al [20] first carried out a method based on deep learning algorithm to detect series arc faults in ac system.…”

Section: Introductionmentioning

confidence: 99%

Identification Method for Series Arc Faults Based on Wavelet Transform and Deep Neural Network

Yang

2019

Energies

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The power supply quality and power supply safety of a low-voltage residential power distribution system is seriously affected by the occurrence of series arc faults. It is difficult to detect and extinguish them due to the characteristics of small current, high stochasticity, and strong concealment. In order to improve the overall safety of residential distribution systems, a novel method based on discrete wavelet transform (DWT) and deep neural network (DNN) is proposed to detect series arc faults in this paper. An experimental bed is built to obtain current signals under two states, normal and arcing. The collected signals are discomposed in different scales applying the DWT. The wavelet coefficient sequences are used for forming training set and test set. The deep neural network trained by training set under 4 different loads adaptively learn the feature of arc faults. The accuracy of arc faults recognition is sent through feeding test set into the model, about 97.75%. The experimental result shows that this method has good accuracy and generality under different types of loading.

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“…For electrical fires caused by short circuits of arc faults, traditional electric circuit protection devices cannot be relied upon for early detection. Most methods for detecting arc faults are based on measurement of the state characteristics of the circuit current [ 4 ]. Typical diagnostic methods include frequency domain analyses [ 5 ], time domain waveform analyses [ 6 ], analyses of autoregressive model parameters [ 7 ] and high-order spectral analyses [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

An arc fault diagnosis algorithm using multiinformation fusion and support vector machines

Yang¹,

Fang²,

Zhang³

et al. 2018

R. Soc. open sci.

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Arc faults in low-voltage electrical circuits are the main hidden cause of electric fires. Accurate identification of arc faults is essential for safe power consumption. In this paper, a detection algorithm for arc faults is tested in a low-voltage circuit. With capacitance coupling and a logarithmic detector, the high-frequency radiation characteristics of arc faults can be extracted. A rapid method for computing the current waveform slope characteristics of an arc fault provides another characteristic. Current waveform periodic integral characteristics can be extracted according to asymmetries of the arc faults. These three characteristics are used to develop a detection algorithm of arc faults based on multiinformation fusion and support vector machine learning models. The tests indicated that for series arc faults with single and combination loads and for parallel arc faults between metallic contacts and along carbonization paths, the recognition algorithm could effectively avoid the problems of crosstalk and signal loss during arc fault detection.

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Real-time Deep Neural Networks for internet-enabled arc-fault detection

Cited by 77 publications

References 8 publications

Automated non-destructive inspection of Fused Filament Fabrication components using Thermographic Signal Reconstruction

Automated non-destructive inspection of Fused Filament Fabrication components using Thermographic Signal Reconstruction

Identification Method for Series Arc Faults Based on Wavelet Transform and Deep Neural Network

An arc fault diagnosis algorithm using multiinformation fusion and support vector machines

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