000 03620nam a22005775i 4500
001 978-3-319-05380-6
003 DE-He213
005 20200421112549.0
007 cr nn 008mamaa
008 140401s2014 gw | s |||| 0|eng d
020 _a9783319053806
_9978-3-319-05380-6
024 7 _a10.1007/978-3-319-05380-6
_2doi
050 4 _aQA76.9.M35
072 7 _aGPFC
_2bicssc
072 7 _aTEC000000
_2bisacsh
082 0 4 _a620
_223
100 1 _aYang, Fan.
_eauthor.
245 1 0 _aCapturing Connectivity and Causality in Complex Industrial Processes
_h[electronic resource] /
_cby Fan Yang, Ping Duan, Sirish L. Shah, Tongwen Chen.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXIII, 91 p. 54 illus., 24 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-530X
505 0 _aIntroduction -- Examples of Applications for Connectivity and Causality Analysis -- Description of Connectivity and Causality -- Capturing Connectivity and Causality from Process Knowledge -- Capturing Causality from Process Data -- Case Studies.
520 _aThis brief reviews concepts of inter-relationship in modern industrial processes, biological and social systems. Specifically ideas of connectivity and causality within and between elements of a complex system are treated; these ideas are of great importance in analysing and influencing mechanisms, structural properties and their dynamic behaviour, especially for fault diagnosis and hazard analysis. Fault detection and isolation for industrial processes being concerned with root causes and fault propagation, the brief shows that, process connectivity and causality information can be captured in two ways: �      from process knowledge: structural modeling based on first-principles structural models can be merged with adjacency/reachability matrices or topology models obtained from process flow-sheets described in standard formats; and �      from process data: cross-correlation analysis, Granger causality and its extensions, frequency domain methods, information-theoretical methods, and Bayesian networks can be used to identify pair-wise relationships and network topology. These methods rely on the notion of information fusion whereby process operating data is combined with qualitative process knowledge, to give a holistic picture of the system.
650 0 _aEngineering.
650 0 _aChemical engineering.
650 0 _aMathematical models.
650 0 _aStatistics.
650 0 _aComplexity, Computational.
650 0 _aControl engineering.
650 1 4 _aEngineering.
650 2 4 _aComplexity.
650 2 4 _aMathematical Modeling and Industrial Mathematics.
650 2 4 _aControl.
650 2 4 _aIndustrial Chemistry/Chemical Engineering.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
700 1 _aDuan, Ping.
_eauthor.
700 1 _aShah, Sirish L.
_eauthor.
700 1 _aChen, Tongwen.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319053790
830 0 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-530X
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-05380-6
912 _aZDB-2-ENG
942 _cEBK
999 _c58779
_d58779