{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T17:06:18Z","timestamp":1769879178746,"version":"3.49.0"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,18]]},"DOI":"10.1109\/ijcnn55064.2022.9892755","type":"proceedings-article","created":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T19:56:04Z","timestamp":1664567764000},"page":"1-8","source":"Crossref","is-referenced-by-count":2,"title":["Detection of bearing failures using wavelet transformation and machine learning approach"],"prefix":"10.1109","author":[{"given":"Maciej","family":"Golgowski","sequence":"first","affiliation":[{"name":"Military University of Technology,Warsaw,POLAND"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stanislaw","family":"Osowski","sequence":"additional","affiliation":[{"name":"Warsaw University of Technology, Military University of Technology,Warsaw,POLAND"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","article-title":"Master machine learning algorithms","author":"brownlee","year":"2020","journal-title":"EBook"},{"key":"ref11","year":"2018","journal-title":"Bearing Vibration Data under Time-varying Rotational Speed Conditions"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611970104"},{"key":"ref13","author":"mertins","year":"1999","journal-title":"Signal analysis wavelets filter banks time frequency transform and applications"},{"key":"ref14","author":"mallat","year":"1999","journal-title":"A Wavelet Tour of Signal Processing"},{"key":"ref15","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref16","article-title":"Chi-squared Statistic","author":"lyons","year":"2015","journal-title":"Practical Cryptography"},{"key":"ref17","year":"2021","journal-title":"MATLAB Wavelet Toolbox"},{"key":"ref18","first-page":"1","article-title":"Image net classification with deep convolutional neural networks","volume":"25","author":"krizhevsky","year":"0","journal-title":"Advances in Neural Information Processing Systems 2012"},{"key":"ref19","first-page":"1","article-title":"Squeezenet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size","author":"iandola","year":"0","journal-title":"Proc Conf ICLR 2017"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1002\/we.2142"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/s21227762"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s11265-018-1378-3"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.21203\/rs.3.rs-678821\/v1"},{"key":"ref8","author":"sch\u00f6lkopf","year":"2002","journal-title":"Learning with kernels"},{"key":"ref7","author":"tan","year":"2014","journal-title":"Introduction to Data Mining"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s11265-019-01461-w"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3389\/fenrg.2021.799039"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"ref20","author":"howard","year":"2017","journal-title":"Mobilenets Efficient convolutional neural networks for mobile vision applications"},{"key":"ref22","author":"szegedy","year":"2016","journal-title":"Inception-v4 inception-resnet and the impact of residual connections on learning"},{"key":"ref21","author":"zhang","year":"2017","journal-title":"ShuffleNet An Extremely Efficient Convolutional Neural Network for Mobile Devices"},{"key":"ref23","author":"tan","year":"2020","journal-title":"Efficientnet Rethinking model scaling for convolutional neural networks"}],"event":{"name":"2022 International Joint Conference on Neural Networks (IJCNN)","location":"Padua, Italy","start":{"date-parts":[[2022,7,18]]},"end":{"date-parts":[[2022,7,23]]}},"container-title":["2022 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9891857\/9889787\/09892755.pdf?arnumber=9892755","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T22:57:40Z","timestamp":1667516260000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9892755\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/ijcnn55064.2022.9892755","relation":{},"subject":[],"published":{"date-parts":[[2022,7,18]]}}}