LEADER 02992oam 2200565I 450 001 9910800185003321 005 20230807204141.0 010 $a1-4987-6019-8 010 $a0-429-10232-1 010 $a1-4665-8406-8 024 7 $a10.1201/b18384 035 $a(CKB)2670000000560202 035 $a(EBL)1707786 035 $a(SSID)ssj0001535645 035 $a(PQKBManifestationID)11841000 035 $a(PQKBTitleCode)TC0001535645 035 $a(PQKBWorkID)11499746 035 $a(PQKB)11057517 035 $a(MiAaPQ)EBC1707786 035 $a(OCoLC)907924065 035 $a(EXLCZ)992670000000560202 100 $a20180331h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aArtificial intelligence tools $edecision support systems in condition monitoring and diagnosis /$fDiego Galar Pascual 210 1$aBoca Raton, Florida :$cCRC Press,$d[2015] 210 4$d©2015 215 $a1 online resource (528 p.) 300 $aDescription based upon print version of record. 311 $a1-4665-8405-X 320 $aIncludes bibliographical references at the end of each chapters. 327 $aFront Cover; Contents; Preface; Acknowledgments; Author; Chapter 1: Massive Field Data Collection: Issues and Challenges; Chapter 2: Condition Monitoring: Available Techniques; Chapter 3: Challenges of Condition Monitoring Using AI Techniques; Chapter 4: Input and Output Data; Chapter 5: Two-Stage Response Surface Approaches to Modeling Drug Interaction; Chapter 6: Nearest Neighbor-Based Techniques; Chapter 7: Cluster-Based Techniques; Chapter 8: Statistical Techniques; Chapter 9: Information Theory-Based Techniques; Chapter 10: Uncertainty Management; Back Cover 330 $aArtificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis discusses various white- and black-box approaches to fault diagnosis in condition monitoring (CM). This indispensable resource:Addresses nearest-neighbor-based, clustering-based, statistical, and information theory-based techniquesConsiders the merits of each technique as well as the issues associated with real-life applicationCovers classification methods, from neural networks to Bayesian and support vector machinesProposes fuzzy logic to explain the uncertainties associated with diagnostic processes 606 $aIndustrial equipment$xMaintenance and repair$xData processing 606 $aMachinery$xMonitoring 606 $aArtificial intelligence$xIndustrial applications 615 0$aIndustrial equipment$xMaintenance and repair$xData processing. 615 0$aMachinery$xMonitoring. 615 0$aArtificial intelligence$xIndustrial applications. 676 $a658.2020285 700 $aPascual$b Diego Galar$01587190 801 0$bFlBoTFG 801 1$bFlBoTFG 906 $aBOOK 912 $a9910800185003321 996 $aArtificial intelligence tools$93874642 997 $aUNINA LEADER 01430nam0-22005171i-450 001 990000423380403321 005 20251111114132.0 010 $a88-420-4177-7 035 $a000042338 035 $aFED01000042338 035 $a(Aleph)000042338FED01 100 $a20020821d1993----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $aa---a---001yy 200 1 $a<>cittą nella storia d'Europa$fLeonardo Benevolo 210 $aRoma$aBari$cEditori Laterza$d1993 215 $aVIII, 236 p.$cill.$d21 cm 225 1 $aFare l'Europa 610 0 $aCittą$aEvoluzione urbana 676 $a710 676 $a711.309 45 676 $a711.409 700 1$aBenevolo,$bLeonardo$f<1923- >$0157 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000423380403321 952 $a08 N 278$b63$fDINED 952 $aURB.LE B 934$b4870$fFARBC 952 $a342036$b1654$fDCATA 952 $aURB.LE B 1205$b5196$fFARBC 952 $aFONDO ROSSI 615$bROSSI 654$fFARBC 952 $aXI A 2121$b21113$fFSPBC 952 $a01 C I 17$b02381$fDINST 952 $aII(FN) A 314$b2978$fDCATA 952 $aFL STO 98$bFL-391$fDECBC 952 $aDE FUSCO 1690$bRDF 1749$fDARST 959 $aDARST 959 $aDINED 959 $aFARBC 959 $aDCATA 959 $aFSPBC 959 $aDINST 959 $aDECBC 996 $aCittą nella storia d'Europa$975470 997 $aUNINA