{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:37:41Z","timestamp":1773931061623,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,26]],"date-time":"2019-12-26T00:00:00Z","timestamp":1577318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The  Fujian Natural Science Foundation","award":["2018J05082"],"award-info":[{"award-number":["2018J05082"]}]},{"name":"the Quanzhou City Science &amp; Technology Program of China","award":["2018C117R"],"award-info":[{"award-number":["2018C117R"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>AC arc faults are one of the most important causes of residential electrical wiring fires, which may produce extremely high temperatures and easily ignite surrounding combustible materials. The global interest in machine learning-based methods for arc fault diagnosis applications is increasing due to continuous challenges in efficiency and accuracy. In this paper, a temporal domain visualization convolutional neural network (TDV-CNN) methodology is proposed. The current transformer and high-speed data acquisition system are used to collect the current of a series of arc faults, then the signal is filtered by a digital filter and converted into a gray image in time sequence before being fed into TDV-CNN. Five different electric loads were selected for experimental validation with various signal characteristics, including vacuum cleaner, fluorescent lamp, dimmer, heater, and desktop computer. The experimental results confirm that the classification accuracy of the five loads\u2019 work states in the ten categories could reach 98.7% or even higher by adjusting parameters perfectly. The methodology is believed to be reliable for series arc detection with relatively high accuracy and also has important potential applications in other fault diagnosis fields.<\/jats:p>","DOI":"10.3390\/s20010162","type":"journal-article","created":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T05:37:08Z","timestamp":1577425028000},"page":"162","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["A Novel Methodology for Series Arc Fault Detection by Temporal Domain Visualization and Convolutional Neural Network"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9820-3150","authenticated-orcid":false,"given":"Kai","family":"Yang","sequence":"first","affiliation":[{"name":"Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment (Huaqiao University), Fujian Province University, Xiamen 361021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3102-9845","authenticated-orcid":false,"given":"Ruobo","family":"Chu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment (Huaqiao University), Fujian Province University, Xiamen 361021, China"},{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Guangzhou 511458, China"}]},{"given":"Rencheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment (Huaqiao University), Fujian Province University, Xiamen 361021, China"}]},{"given":"Jinchao","family":"Xiao","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Guangzhou 511458, China"}]},{"given":"Ran","family":"Tu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment (Huaqiao University), Fujian Province University, Xiamen 361021, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1109\/TIA.1982.4504068","article-title":"The other electrical hazard: Electric arc blast burns","volume":"IA-18","author":"Lee","year":"1982","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Du, J.H., Tu, R., Zeng, Y., Pan, L., and Zhang, R.C. (2017). An experimental study on the thermal characteristics and heating effect of arc-fault from Cu core in residential electrical wiring fires. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0182811"},{"key":"ref_3","unstructured":"(2019, March 29). China National Fire and Police Situation in 2018, Available online: http:\/\/www.119.gov.cn\/xiaofang\/hztj\/36306.htm."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2526","DOI":"10.1109\/TIM.2018.2826878","article-title":"A new methodology for identifying arc fault by sparse representation and neural network","volume":"67","author":"Wang","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ji, H.K., Wang, G.M., Kim, W.H., and Kil, G.S. (2018). Optimal design of a band pass filter and an algorithm for series arc detection. Energies, 11.","DOI":"10.3390\/en11040992"},{"key":"ref_6","unstructured":"Underwriters Laboratories Inc. (2011). UL Standard for Arc-Fault Circuit-Interrupters, Underwriters Laboratories Inc.. [2nd ed.]."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1109\/TPWRD.2004.832407","article-title":"A new PMU-based fault detection\/location technique for transmission lines with consideration of arcing fault discrimination-part I: Theory and algorithms","volume":"19","author":"Lin","year":"2004","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1109\/TPEL.2006.872385","article-title":"Arc fault detection scheme for 42-V automotive DC networks using current shunt","volume":"21","author":"Naidu","year":"2006","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1109\/TIA.2004.831287","article-title":"More about arc-fault circuit interrupters","volume":"40","author":"Gregory","year":"2004","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_10","first-page":"71","article-title":"Series arc fault characteristics based on gray level-gradient co-occurrence matrix","volume":"33","author":"Guo","year":"2018","journal-title":"Trans. Chin. Electrotech. Soc."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lu, Q., Ye, Z., Zhang, Y., Wang, T., and Gao, Z. (2019). Analysis of the effects of arc volt\u2013ampere characteristics on different loads and detection methods of series arc faults. Energies, 12.","DOI":"10.3390\/en12020323"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.rser.2018.03.010","article-title":"A comprehensive review on DC arc faults and their diagnosis methods in photovoltaic systems","volume":"89","author":"Lu","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, K., Zhang, R., Yang, J., Liu, C., Chen, S., and Zhang, F. (2016). A novel arc fault detector for early detection of electrical fires. Sensors, 16.","DOI":"10.3390\/s16040500"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"929","DOI":"10.3390\/a8040929","article-title":"Series arc fault detection algorithm based on autoregressive bispectrum analysis","volume":"8","author":"Yang","year":"2015","journal-title":"Algorithms"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhang, X., Dong, Y., and Li, W. (2016, January 10\u201312). Characteristics analysis and detection of AC arc fault in SSPC based on wavelet transform. Proceedings of the 2016 IEEE International Conference on Aircraft Utility Systems (AUS), Beijing, China.","DOI":"10.1109\/AUS.2016.7748097"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/TIM.2016.2627248","article-title":"Arc fault detection method based on CZT low-frequency harmonic current analysis","volume":"66","author":"Artale","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Artale, G., Cataliotti, A., Nuccio, V.C.S., Cara, D.D., Tin\u00e8, G., and Privitera, G. (2016, January 23\u201326). A set of indicators for arc faults detection based on low frequency harmonic analysis. Proceedings of the 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings, Taipei, Taiwan.","DOI":"10.1109\/I2MTC.2016.7520536"},{"key":"ref_18","first-page":"49","article-title":"Method of Low-voltage Arc Fault Recognition Using High Frequency Feature","volume":"28","author":"Gao","year":"2016","journal-title":"Proc. CSU-EPSA"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"45831","DOI":"10.1109\/ACCESS.2019.2909267","article-title":"DA-DCGAN: An effective methodology for DC series arc fault diagnosis in photovoltaic systems","volume":"7","author":"Lu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_22","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, The MIT Press."},{"key":"ref_23","first-page":"396","article-title":"Handwritten digit recognition with a back-propagation network","volume":"2","author":"Cun","year":"1990","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","unstructured":"Kingma, D., and Ba, J. (2014). Adam: Amethod for stochastic optimization. arXiv."},{"key":"ref_25","unstructured":"General Administration of Quality Supervision, Inspection and Quarantine of the People\u2019s Republic of China (2014). Electrical Fire Monitoring System-Part 4: Arcing Fault Detectors (GB14287.4-2014), Standards Press of China."},{"key":"ref_26","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.ijepes.2019.05.012","article-title":"Multi criteria series arc fault detection based on supervised feature selection","volume":"113","author":"Vu","year":"2019","journal-title":"Int. J. Electr. Power Energy Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/162\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:45:54Z","timestamp":1760190354000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/162"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,26]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20010162"],"URL":"https:\/\/doi.org\/10.3390\/s20010162","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,26]]}}}