{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T13:46:33Z","timestamp":1770299193079,"version":"3.49.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T00:00:00Z","timestamp":1646697600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T00:00:00Z","timestamp":1646697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Faculty of Graduate Studies, University of Manitoba"},{"name":"Faculty of Graduate Studies, University of Manitoba"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s00521-022-07066-y","type":"journal-article","created":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T15:03:02Z","timestamp":1646751782000},"page":"11739-11750","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["An interpretable CNN model for classification of partial discharge waveforms in 3D-printed dielectric samples with different void sizes"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1798-0351","authenticated-orcid":false,"given":"Sara","family":"Mantach","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8886-9533","authenticated-orcid":false,"given":"Puneet","family":"Gill","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4552-8928","authenticated-orcid":false,"given":"Derek R.","family":"Oliver","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0463-4102","authenticated-orcid":false,"given":"Ahmed","family":"Ashraf","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5035-3291","authenticated-orcid":false,"given":"Behzad","family":"Kordi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,8]]},"reference":[{"issue":"2","key":"7066_CR1","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1109\/94.300259","volume":"1","author":"L Satish","year":"1994","unstructured":"Satish L, Zaengl WS (1994) Artificial neural networks for recognition of 3-d partial discharge patterns. IEEE Trans Dielectr Electr Insul 1(2):265\u2013275","journal-title":"IEEE Trans Dielectr Electr Insul"},{"key":"7066_CR2","volume-title":"Dielectric breakdown of solids","author":"S Whitehead","year":"1951","unstructured":"Whitehead S (1951) Dielectric breakdown of solids. Clarendon Press, London"},{"issue":"2","key":"7066_CR3","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1109\/14.90292","volume":"24","author":"GC Crichton","year":"1989","unstructured":"Crichton GC, Karlsson P, Pedersen A (1989) Partial discharges in ellipsoidal and spheroidal voids. IEEE Trans Electr Insul 24(2):335\u2013342","journal-title":"IEEE Trans Electr Insul"},{"key":"7066_CR4","doi-asserted-by":"crossref","unstructured":"Fothergill JC (2007) Ageing, space charge and nanodielectrics: ten things we don\u2019t know about dielectrics. In: 2007 IEEE international conference on solid dielectrics. IEEE, pp 1\u201310","DOI":"10.1109\/ICSD.2007.4290739"},{"key":"7066_CR5","doi-asserted-by":"crossref","unstructured":"Zhang Y, He L, Zhu H (2017) Influencing factors of partial discharge of needle-plate based on acoustic emission detection. In: World conference on acoustic emission. Springer, pp 389\u2013397","DOI":"10.1007\/978-3-030-12111-2_36"},{"issue":"5","key":"7066_CR6","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1109\/94.469976","volume":"2","author":"A Krivda","year":"1995","unstructured":"Krivda A (1995) Automated recognition of partial discharges. IEEE Trans Dielectr Electr Insul 2(5):796\u2013821","journal-title":"IEEE Trans Dielectr Electr Insul"},{"issue":"2","key":"7066_CR7","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1109\/TDEI.2005.1430395","volume":"12","author":"N Sahoo","year":"2005","unstructured":"Sahoo N, Salama M, Bartnikas R (2005) Trends in partial discharge pattern classification: a survey. IEEE Trans Dielectr Electr Insul 12(2):248\u2013264","journal-title":"IEEE Trans Dielectr Electr Insul"},{"issue":"2","key":"7066_CR8","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1109\/72.991432","volume":"13","author":"C-F Lin","year":"2002","unstructured":"Lin C-F, Wang S-D (2002) Fuzzy support vector machines. IEEE Trans Neural Netw 13(2):464\u2013471","journal-title":"IEEE Trans Neural Netw"},{"key":"7066_CR9","first-page":"16","volume-title":"Pattern classification","author":"RO Duda","year":"2001","unstructured":"Duda RO, Hart PE, Stork DG (2001) Pattern classification, vol 58, 2nd edn. Wiley, New York, p 16","edition":"2"},{"issue":"1","key":"7066_CR10","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/0893-6080(90)90049-Q","volume":"3","author":"DF Specht","year":"1990","unstructured":"Specht DF (1990) Probabilistic neural networks. Neural Netw 3(1):109\u2013118","journal-title":"Neural Netw"},{"issue":"1","key":"7066_CR11","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119\u2013139","journal-title":"J Comput Syst Sci"},{"issue":"6","key":"7066_CR12","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MEI.2015.7303259","volume":"31","author":"M Wu","year":"2015","unstructured":"Wu M, Cao H, Cao J, Nguyen H-L, Gomes JB, Krishnaswamy SP (2015) An overview of state-of-the-art partial discharge analysis techniques for condition monitoring. IEEE Electr Insul Mag 31(6):22\u201335","journal-title":"IEEE Electr Insul Mag"},{"key":"7066_CR13","doi-asserted-by":"crossref","unstructured":"Catterson V, Sheng B (2015) Deep neural networks for understanding and diagnosing partial discharge data. In: 2015 IEEE electrical insulation conference (EIC). IEEE, pp 218\u2013221","DOI":"10.1109\/ICACACT.2014.7223616"},{"issue":"6","key":"7066_CR14","doi-asserted-by":"publisher","first-page":"1861","DOI":"10.1109\/TDEI.2020.009070","volume":"27","author":"S Lu","year":"2020","unstructured":"Lu S, Chai H, Sahoo A, Phung B (2020) Condition monitoring based on partial discharge diagnostics using machine learning methods: a comprehensive state-of-the-art review. IEEE Trans Dielectr Electr Insul 27(6):1861\u20131888","journal-title":"IEEE Trans Dielectr Electr Insul"},{"issue":"24","key":"7066_CR15","doi-asserted-by":"publisher","first-page":"4674","DOI":"10.3390\/en12244674","volume":"12","author":"Y Wang","year":"2019","unstructured":"Wang Y, Yan J, Yang Z, Liu T, Zhao Y, Li J (2019) Partial discharge pattern recognition of gas-insulated switchgear via a light-scale convolutional neural network. Energies 12(24):4674","journal-title":"Energies"},{"issue":"13","key":"7066_CR16","doi-asserted-by":"publisher","first-page":"2485","DOI":"10.3390\/en12132485","volume":"12","author":"S Barrios","year":"2019","unstructured":"Barrios S, Buldain D, Comech MP, Gilbert I, Orue I (2019) Partial discharge classification using deep learning methods-survey of recent progress. Energies 12(13):2485","journal-title":"Energies"},{"issue":"4","key":"7066_CR17","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","volume":"33","author":"HI Fawaz","year":"2019","unstructured":"Fawaz HI, Forestier G, Weber J, Idoumghar L, Muller P-A (2019) Deep learning for time series classification: a review. Data Min Knowl Discov 33(4):917\u2013963","journal-title":"Data Min Knowl Discov"},{"key":"7066_CR18","doi-asserted-by":"crossref","unstructured":"Ibrahim A, Zhou Y, Jenkins ME, Trejos AL, Naish MD (2020) The design of a parkinson\u2019s tremor predictor and estimator using a hybrid convolutional-multilayer perceptron neural network. In: 2020 42nd annual international conference of the ieee engineering in medicine & biology society (EMBC). IEEE, pp 5996\u20136000","DOI":"10.1109\/EMBC44109.2020.9176132"},{"issue":"3","key":"7066_CR19","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1007\/s42835-019-00115-y","volume":"14","author":"MA Khan","year":"2019","unstructured":"Khan MA, Choo J, Kim Y-H (2019) End-to-end partial discharge detection in power cables via time-domain convolutional neural networks. J Electr Eng Technol 14(3):1299\u20131309","journal-title":"J Electr Eng Technol"},{"key":"7066_CR20","doi-asserted-by":"crossref","unstructured":"Wang W, Yu N (2020) Partial discharge detection with convolutional neural networks. In: 2020 international conference on probabilistic methods applied to power systems (PMAPS). IEEE, pp 1\u20136","DOI":"10.1109\/PMAPS47429.2020.9183469"},{"issue":"4","key":"7066_CR21","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.1109\/TPWRD.2019.2906086","volume":"34","author":"X Peng","year":"2019","unstructured":"Peng X, Yang F, Wang G, Wu Y, Li L, Li Z, Bhatti AA, Zhou C, Hepburn DM, Reid AJ et al (2019) A convolutional neural network-based deep learning methodology for recognition of partial discharge patterns from high-voltage cables. IEEE Trans Power Deliv 34(4):1460\u20131469","journal-title":"IEEE Trans Power Deliv"},{"key":"7066_CR22","doi-asserted-by":"crossref","unstructured":"Borghei M, Ghassemi M, Kordi B, Gill P, Oliver D (2021) A finite element analysis model for internal partial discharges in an air-filled cylindrical cavity inside solid dielectric. In: IEEE electrical insulation conference (EIC), pp 7\u201321","DOI":"10.1109\/EIC49891.2021.9612268"},{"issue":"3","key":"7066_CR23","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1109\/TDEI.2002.1007695","volume":"9","author":"A Contin","year":"2002","unstructured":"Contin A, Cavallini A, Montanari G, Pasini G, Puletti F (2002) Digital detection and fuzzy classification of partial discharge signals. IEEE Trans Dielectr Electr Insul 9(3):335\u2013348","journal-title":"IEEE Trans Dielectr Electr Insul"},{"issue":"2","key":"7066_CR24","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1109\/MEI.2003.1192033","volume":"19","author":"A Cavallini","year":"2003","unstructured":"Cavallini A, Montanari G, Contin A, Pulletti F (2003) A new approach to the diagnosis of solid insulation systems based on pd signal inference. IEEE Electr Insul Mag 19(2):23\u201330","journal-title":"IEEE Electr Insul Mag"},{"issue":"2","key":"7066_CR25","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1109\/TDEI.2005.1430391","volume":"12","author":"A Cavallini","year":"2005","unstructured":"Cavallini A, Montanari G, Puletti F, Contin A (2005) A new methodology for the identification of pd in electrical apparatus: properties and applications. IEEE Trans Dielectr Electr Insul 12(2):203\u2013215","journal-title":"IEEE Trans Dielectr Electr Insul"},{"key":"7066_CR26","unstructured":"Janani H (2016) Partial discharge source classification using pattern recognition algorithms. PhD thesis, University of Manitoba"},{"key":"7066_CR27","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inf Process Syst"},{"key":"7066_CR28","doi-asserted-by":"publisher","first-page":"101393","DOI":"10.1016\/j.pmcj.2021.101393","volume":"73","author":"X Shen","year":"2021","unstructured":"Shen X, Ni Z, Liu L, Yang J, Ahmed K (2021) Wipass: 1d-cnn-based smartphone keystroke recognition using wifi signals. Pervasive Mob Comput 73:101393","journal-title":"Pervasive Mob Comput"},{"key":"7066_CR29","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1016\/j.neucom.2019.06.032","volume":"359","author":"W Chen","year":"2019","unstructured":"Chen W, Shi K (2019) A deep learning framework for time series classification using relative position matrix and convolutional neural network. Neurocomputing 359:384\u2013394","journal-title":"Neurocomputing"},{"key":"7066_CR30","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Icml"},{"key":"7066_CR31","first-page":"646","volume":"22","author":"I Goodfellow","year":"2009","unstructured":"Goodfellow I, Lee H, Le Q, Saxe A, Ng A (2009) Measuring invariances in deep networks. Adv Neural Inf Process Syst 22:646\u2013654","journal-title":"Adv Neural Inf Process Syst"},{"key":"7066_CR32","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2014) Semantic image segmentation with deep convolutional nets and fully connected crfs. Preprint arXiv:1412.7062"},{"key":"7066_CR33","doi-asserted-by":"crossref","unstructured":"Yi-de M, Qing L, Zhi-Bai Q (2004) Automated image segmentation using improved pcnn model based on cross-entropy. In: Proceedings of 2004 international symposium on intelligent multimedia, video and speech processing. IEEE, pp 743\u2013746","DOI":"10.1109\/ISIMP.2004.1434171"},{"key":"7066_CR34","doi-asserted-by":"crossref","unstructured":"Zhang Z (2018) Improved adam optimizer for deep neural networks. In: 2018 IEEE\/ACM 26th international symposium on quality of service (IWQoS). IEEE, pp 1\u20132","DOI":"10.1109\/IWQoS.2018.8624183"},{"issue":"3","key":"7066_CR35","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1093\/biomet\/76.3.503","volume":"76","author":"P Burman","year":"1989","unstructured":"Burman P (1989) A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika 76(3):503\u2013514","journal-title":"Biometrika"},{"issue":"8","key":"7066_CR36","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798\u20131828","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7066_CR37","unstructured":"Jetley S, Lord NA, Lee N, Torr PH (2018) Learn to pay attention. Preprint arXiv:1804.02391"},{"issue":"1","key":"7066_CR38","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1145\/3359786","volume":"63","author":"M Du","year":"2019","unstructured":"Du M, Liu N, Hu X (2019) Techniques for interpretable machine learning. Commun ACM 63(1):68\u201377","journal-title":"Commun ACM"},{"key":"7066_CR39","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618\u2013626","DOI":"10.1109\/ICCV.2017.74"},{"issue":"6","key":"7066_CR40","doi-asserted-by":"publisher","first-page":"1762","DOI":"10.1109\/JBHI.2019.2949601","volume":"24","author":"T He","year":"2019","unstructured":"He T, Guo J, Chen N, Xu X, Wang Z, Fu K, Liu L, Yi Z (2019) Medimlp: using grad-cam to extract crucial variables for lung cancer postoperative complication prediction. IEEE J Biomed Health Inform 24(6):1762\u20131771","journal-title":"IEEE J Biomed Health Inform"},{"key":"7066_CR41","doi-asserted-by":"publisher","first-page":"110190","DOI":"10.1016\/j.chaos.2020.110190","volume":"140","author":"H Panwar","year":"2020","unstructured":"Panwar H, Gupta P, Siddiqui MK, Morales-Menendez R, Bhardwaj P, Singh V (2020) A deep learning and grad-cam based color visualization approach for fast detection of covid-19 cases using chest x-ray and ct-scan images. Chaos Solitons Fractals 140:110190","journal-title":"Chaos Solitons Fractals"},{"key":"7066_CR42","unstructured":"Cian D, van Gemert J, Lengyel A (2020) Evaluating the performance of the lime and grad-cam explanation methods on a lego multi-label image classification task. Preprint arXiv:2008.01584"},{"issue":"11","key":"7066_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40430-020-02688-6","volume":"42","author":"F Feng","year":"2020","unstructured":"Feng F, Wu C, Zhu J, Wu S, Tian Q, Jiang P (2020) Research on multitask fault diagnosis and weight visualization of rotating machinery based on convolutional neural network. J Braz Soc Mech Sci Eng 42(11):1\u201314","journal-title":"J Braz Soc Mech Sci Eng"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07066-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07066-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07066-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T09:28:03Z","timestamp":1657877283000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07066-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,8]]},"references-count":43,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["7066"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07066-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,8]]},"assertion":[{"value":"3 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}