LEADER 01907oam 2200529 450 001 9910716823803321 005 20211229114659.0 035 $a(CKB)5470000002525951 035 $a(OCoLC)1275381846 035 $a(EXLCZ)995470000002525951 100 $a20211013j202102 ua 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine learning for gearbox fault prediction by using both SCADA and modeled data /$fLindy Williams [and four others] 210 1$a[Golden, CO] :$cNational Renewable Energy Laboratory,$d[February 2021]. 215 $a1 online resource (19 pages) $ccolor illustrations 225 1 $aNREL/PR ;$v2C00-79167 300 $a"Drivetrain Reliability Collaborative Workshop."' 300 $a"February 16-17, 2021." 320 $aIncludes bibliographical references (page 19). 517 3 $aMachine learning for gearbox fault prediction by using both supervisory control and data acquisition and modeled data 606 $aMachine learning 606 $aSupervisory control systems$zUnited States 606 $aWind turbines$zUnited States 606 $aMachine learning$2fast 606 $aSupervisory control systems$2fast 606 $aWind turbines$2fast 607 $aUnited States$2fast 615 0$aMachine learning. 615 0$aSupervisory control systems 615 0$aWind turbines 615 7$aMachine learning. 615 7$aSupervisory control systems. 615 7$aWind turbines. 700 $aWilliams$b Lindy$01408126 712 02$aNational Renewable Energy Laboratory (U.S.), 801 0$bGPO 801 1$bGPO 801 2$bOCLCF 801 2$bGPO 906 $aBOOK 912 $a9910716823803321 996 $aMachine learning for gearbox fault prediction by using both SCADA and modeled data$93491254 997 $aUNINA