LEADER 03621nam 2200529 450 001 9910555073903321 005 20210610082428.0 010 $a1-119-35670-9 010 $a1-119-35669-5 010 $a1-119-35667-9 035 $a(CKB)4100000007823717 035 $a(MiAaPQ)EBC5744605 035 $a(CaSebORM)9781119356653 035 $a(EXLCZ)994100000007823717 100 $a20190422d2019 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPrognostics and health management $ea practical approach to improving system reliability using conditioned-based data /$fDouglas Goodman, James P. Hofmeister and Ferenc Szidarovszky 205 $a1st edition 210 1$aHoboken, New Jersey ;$aChichester, West Sussex, England :$cWiley,$d[2019] 210 4$dİ2019 215 $a1 online resource (385 pages) 311 $a1-119-35665-2 320 $aIncludes bibliographical references and index. 330 $aA comprehensive guide to the application and processing of condition-based data to produce prognostic estimates of functional health and life. Prognostics and Health Management provides an authoritative guide for an understanding of the rationale and methodologies of a practical approach for improving system reliability using conditioned-based data (CBD) to the monitoring and management of health of systems. This proven approach uses electronic signatures extracted from conditioned-based electrical signals, including those representing physical components, and employs processing methods that include data fusion and transformation, domain transformation, and normalization, canonicalization and signal-level translation to support the determination of predictive diagnostics and prognostics. Written by noted experts in the field, Prognostics and Health Management clearly describes how to extract signatures from conditioned-based data using conditioning methods such as data fusion and transformation, domain transformation, data type transformation and indirect and differential comparison. This important resource: Integrates data collecting, mathematical modelling and reliability prediction in one volume Contains numerical examples and problems with solutions that help with an understanding of the algorithmic elements and processes Presents information from a panel of experts on the topic Follows prognostics based on statistical modelling, reliability modelling and usage modelling methods Written for system engineers working in critical process industries and automotive and aerospace designers, Prognostics and Health Management offers a guide to the application of condition-based data to produce signatures for input to predictive algorithms to produce prognostic estimates of functional health and life. 606 $aMachinery$xReliability 606 $aEquipment health monitoring 606 $aMachinery$xMaintenance and repair$xPlanning 606 $aStructural failures$xMathematical models 615 0$aMachinery$xReliability. 615 0$aEquipment health monitoring. 615 0$aMachinery$xMaintenance and repair$xPlanning. 615 0$aStructural failures$xMathematical models. 676 $a621.816 700 $aGoodman$b Douglas$c(Industrial engineer),$01217997 702 $aHofmeister$b James P. 702 $aSzidarovszky$b Ferenc 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910555073903321 996 $aPrognostics and health management$92816653 997 $aUNINA LEADER 02095oam 2200469 a 450 001 9910698863703321 005 20090513100653.0 035 $a(CKB)5470000002397227 035 $a(OCoLC)226299222 035 $a(EXLCZ)995470000002397227 100 $a20080425d2008 ua 0 101 0 $aeng 135 $aurmn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNanotechnology$b[electronic resource] $ebetter guidance is needed to ensure accurate reporting of federal research focused on environmental, health, and safety risks : report to congressional requesters /$fUnited States Government Accountability Office 210 1$a[Washington, D.C.] :$cU.S. Govt. Accountability Office,$d[2008] 215 $aiii, 37 pages $cdigital, PDF file 300 $aTitle from title screen (viewed on Apr. 25, 2008). 300 $a"March 2008." 300 $aPaper version available from: U.S. Govt. Accountability Office, 441 G St., NW, Rm. LM, Washington, D.C. 20548. 300 $a"GAO-08-402." 320 $aIncludes bibliographical references. 330 $aGAO was asked to determine (1) the extent to which selected agencies conducted [nanotechnology] research in fiscal year 2006; (2) the reasonableness of the agencies' and the NNI's processes to identify and prioritize such federal research; and (3) the effectiveness of the agencies' and the NNI's process to coordinate this research. 517 $aNanotechnology 606 $aNanotechnology$xGovernment policy$zUnited States 606 $aNanotechnology$xResearch$zUnited States 606 $aScience and state$zUnited States 606 $aAdministrative agencies$xResearch$xEvaluation 615 0$aNanotechnology$xGovernment policy 615 0$aNanotechnology$xResearch 615 0$aScience and state 615 0$aAdministrative agencies$xResearch$xEvaluation. 801 0$bEJB 801 1$bEJB 801 2$bOCLCQ 801 2$bGPO 906 $aBOOK 912 $a9910698863703321 996 $aNanotechnology$9328301 997 $aUNINA