000			04047nam a22003735i 4500
001			291435
003			MX-SnUAN
005			20160429154856.0
007			cr nn 008mamaa
008			150903s2008 xxk| o |||| 0|eng d
020			_a9781848001596 _99781848001596
024	7		_a10.1007/9781848001596 _2doi
035			_avtls000344190
039		9	_a201509030353 _bVLOAD _c201405050303 _dVLOAD _y201402061250 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
100	1		_aSamantaray, Arun K. _eautor _9322724
245	1	0	_aModel-based Process Supervision : _bA Bond Graph Approach / _cby Arun K. Samantaray, Belkacem Ould Bouamama.
264		1	_aLondon : _bSpringer London, _c2008.
300			_brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aAdvances in Industrial Control, _x1430-9491
500			_aSpringer eBooks
505	0		_ato Process Supervision -- Bond Graph Modeling in Process Engineering -- Model-based Control -- Bond Graph Model-based Qualitative FDI -- Bond Graph Model-based Quantitative FDI -- Application to a Steam Generator Process -- Diagnostic and Bicausal Bond Graphs for FDI -- Actuator and Sensor Placement for Reconfiguration -- Isolation of Structurally Non-isolatable Faults -- Multiple Fault Isolation Through Parameter Estimation -- Fault Tolerant Control.
520			_aModel-based fault detection and isolation requires a mathematical model of the system behaviour. Modelling is important and can be difficult because of the complexity of the monitored system and its control architecture. The authors use bond-graph modelling, a unified multi-energy domain modelling method, to build dynamic models of process engineering systems by composing hierarchically arranged sub-models of various commonly encountered process engineering devices. The structural and causal properties of bond-graph models are exploited for supervisory systems design. The structural properties of a system, necessary for process control, are elegantly derived from bond-graph models by following the simple algorithms presented here. Additionally, structural analysis of the model, augmented with available instrumentation, indicates directly whether it is possible to detect and/or isolate faults in some specific sub-space of the process. Such analysis aids in the design and resource optimization of new supervision platforms. Static and dynamic constraints, which link the time evolution of the known variables under normal operation, are evaluated in real time to determine faults in the system. Various decision or post-processing steps integral to the supervisory environment are discussed in this monograph; they are required to extract meaningful data from process state knowledge because of unavoidable process uncertainties. Process state knowledge has been further used to take active and passive fault accommodation measures. Several applications to academic and small-scale-industrial processes are interwoven throughout. Finally, an application concerning development of a supervision platform for an industrial plant is presented with experimental validation. Model-based Process Supervision provides control engineers and workers in industrial and academic research establishments interested in process engineering with a means to build up a practical and functional supervisory control environment and to use sophisticated models to get the best use out of their process data.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aBouamama, Belkacem Ould. _eautor _9322725
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781848001589
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-84800-159-6 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942			_c14
999			_c291435 _d291435