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To overcome this problem, this paper proposes a novel method to generative enough label samples for training deep learning models. Unlike the generative adversarial networks, which required complex computing time, the calculation of the proposed novel generative method is simple and effective. First, we calculate the Euclidean distance between the training sample and the test sample; then, the weight coefficient between the training sample and the test sample is settled to generate pseudosamples; finally, combine with the pseudosamples, the deep learning method is training for machine fault diagnosis. In order to verify the effectiveness of the proposed method, two experiment datasets with planetary gearboxes and wind gearboxes are carried out with different activation functions. Experimental results show that the proposed method is effective for most activation function models.<\/jats:p>","DOI":"10.1155\/2022\/5420478","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T19:41:00Z","timestamp":1641930060000},"page":"1-11","source":"Crossref","is-referenced-by-count":3,"title":["A Novel Generative Method for Machine Fault Diagnosis"],"prefix":"10.1155","volume":"2022","author":[{"given":"Zhipeng","family":"Dong","sequence":"first","affiliation":[{"name":"Army Engineering University of PLA, Shijiazhuang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yucheng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Dongguan University of Technology, Dongguan 523808, China"},{"name":"College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7969-2040","authenticated-orcid":true,"given":"Jianshe","family":"Kang","sequence":"additional","affiliation":[{"name":"Army Engineering University of PLA, Shijiazhuang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9784-4546","authenticated-orcid":true,"given":"Shaohui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Dongguan University of Technology, Dongguan 523808, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.107325"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-020-09480-8"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.apacoust.2020.107399"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2019.2912763"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2017.10.019"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.106060"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1007\/s11465-021-0652-4"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2018.10.031"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.3390\/app9163374"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.106683"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3009644"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.12.033"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.07.008"},{"issue":"3","key":"14","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1109\/TII.2020.2991796","article-title":"Deep convolution-based LSTM network for remaining useful life prediction","volume":"17","author":"M. 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