LEADER 01583nam  2200481I  450 
001 9910705789403321
005 20170612112609.0
035   $a(CKB)5470000002452509
035   $a(OCoLC)989874751
035   $a(EXLCZ)995470000002452509
100   $a20170612j201701    ua             0
101 0 $aeng
135   $aurcn|||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aApplication of machine learning to rotorcraft health monitoring /$fTyler Cody, Paula J. Dempsey
210  1$aCleveland Ohio :$cNational Aeronautics and Space Administration, Glenn Research Center,$dJanuary 2017.
215   $a1 online resource (14 pages) $ccolor illustrations
225 1 $aNASA/TM ;$v2017-219408
300   $a"January 2017."
320   $aIncludes bibliographical references (page 14).
606   $aBayes theorem$2nasat
606   $aFatigue (materials)$2nasat
606   $aMachine learning$2nasat
606   $aNeural nets$2nasat
606   $aRotary wing aircraft$2nasat
606   $aTime dependence$2nasat
615  7$aBayes theorem.
615  7$aFatigue (materials)
615  7$aMachine learning.
615  7$aNeural nets.
615  7$aRotary wing aircraft.
615  7$aTime dependence.
700   $aCody$b Tyler$01403193
702   $aDempsey$b Paula J.
712 02$aNASA Glenn Research Center,
801  0$bGPO
801  1$bGPO
906   $aBOOK
912   $a9910705789403321
996   $aApplication of machine learning to rotorcraft health monitoring$93475334
997   $aUNINA