{"title":"用神经网络软件验证电机系统振动数据","authors":"M. Evans, A. Trzynadlowski","doi":"10.1109/IAS.1992.244464","DOIUrl":null,"url":null,"abstract":"Verification of vibration data in electromachine systems using neural network software is described. Due to a number of factors, the data are often corrupted and therefore it has been usually verified by a visual inspection. The developed neural networks were trained to screen out glitched and clipped waveforms. Satisfactory results were obtained, indicating the feasibility of neural networks for vibrational diagnostics.<<ETX>>","PeriodicalId":110710,"journal":{"name":"Conference Record of the 1992 IEEE Industry Applications Society Annual Meeting","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Verification of vibration data in electromachine systems using neural-network software\",\"authors\":\"M. Evans, A. Trzynadlowski\",\"doi\":\"10.1109/IAS.1992.244464\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Verification of vibration data in electromachine systems using neural network software is described. Due to a number of factors, the data are often corrupted and therefore it has been usually verified by a visual inspection. The developed neural networks were trained to screen out glitched and clipped waveforms. Satisfactory results were obtained, indicating the feasibility of neural networks for vibrational diagnostics.<<ETX>>\",\"PeriodicalId\":110710,\"journal\":{\"name\":\"Conference Record of the 1992 IEEE Industry Applications Society Annual Meeting\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Conference Record of the 1992 IEEE Industry Applications Society Annual Meeting\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IAS.1992.244464\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference Record of the 1992 IEEE Industry Applications Society Annual Meeting","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAS.1992.244464","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Verification of vibration data in electromachine systems using neural-network software
Verification of vibration data in electromachine systems using neural network software is described. Due to a number of factors, the data are often corrupted and therefore it has been usually verified by a visual inspection. The developed neural networks were trained to screen out glitched and clipped waveforms. Satisfactory results were obtained, indicating the feasibility of neural networks for vibrational diagnostics.<>