000 03458nam a22005055i 4500
001 978-3-658-15971-9
003 DE-He213
005 20200421112555.0
007 cr nn 008mamaa
008 161004s2016 gw | s |||| 0|eng d
020 _a9783658159719
_9978-3-658-15971-9
024 7 _a10.1007/978-3-658-15971-9
_2doi
050 4 _aQA276-280
072 7 _aUYAM
_2bicssc
072 7 _aUFM
_2bicssc
072 7 _aCOM077000
_2bisacsh
082 0 4 _a005.55
_223
100 1 _aZhang, Kai.
_eauthor.
245 1 0 _aPerformance Assessment for Process Monitoring and Fault Detection Methods
_h[electronic resource] /
_cby Kai Zhang.
264 1 _aWiesbaden :
_bSpringer Fachmedien Wiesbaden :
_bImprint: Springer Vieweg,
_c2016.
300 _aXXI, 153 p. 55 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aAssessing the performance of T2 and Q fault detection statistics -- Proposing a new performance evaluation index called expected detection delay (EDD) -- Assessing the performance of different PM-FD methods using EDD when applied to detecting different types of faults -- Assessing the state-space-based PM-FD methods when applied to a real hot strip mill process.
520 _aThe objective of Kai Zhang and his research is to assess the existing process monitoring and fault detection (PM-FD) methods. His aim is to provide suggestions and guidance for choosing appropriate PM-FD methods, because the performance assessment study for PM-FD methods has become an area of interest in both academics and industry. The author first compares basic FD statistics, and then assesses different PM-FD methods to monitor the key performance indicators of static processes, steady-state dynamic processes and general dynamic processes including transient states. He validates the theoretical developments using both benchmark and real industrial processes. Contents Assessing the performance of T2 and Q fault detection statistics Proposing a new performance evaluation index called expected detection delay (EDD) Assessing the performance of different PM-FD methods using EDD when applied to detecting different types of faults Assessing the state-space-based PM-FD methods when applied to a real hot strip mill process Target Groups Scientists and students in the field of process control and statistical quality control Electrical engineers, chemical engineers, hot strip steel mill engineers About the Author Kai Zhang has just finished his PhD defense. His research area covers multivariate statistical process monitoring (PM) methods, data-driven fault detection (FD) methods and performance evaluation for PM-FD methods.
650 0 _aComputer science.
650 0 _aChemical engineering.
650 0 _aMathematical statistics.
650 0 _aSystem theory.
650 0 _aControl engineering.
650 1 4 _aComputer Science.
650 2 4 _aProbability and Statistics in Computer Science.
650 2 4 _aControl.
650 2 4 _aIndustrial Chemistry/Chemical Engineering.
650 2 4 _aSystems Theory, Control.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783658159702
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-658-15971-9
912 _aZDB-2-SCS
942 _cEBK
999 _c59121
_d59121