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008			150903s2013 xxk| o |||| 0|eng d
020			_a9781447151852 _99781447151852
024	7		_a10.1007/9781447151852 _2doi
035			_avtls000340034
039		9	_a201509030842 _bVLOAD _c201404300409 _dVLOAD _y201402061015 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQ334-342
100	1		_aAldrich, Chris. _eautor _9315260
245	1	0	_aUnsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods / _cby Chris Aldrich, Lidia Auret.
264		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2013.
300			_axIx, 374 páginas 208 ilustraciones, 151 ilustraciones en color. _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 Computer Vision and Pattern Recognition, _x2191-6586
500			_aSpringer eBooks
505	0		_aIntroduction -- Overview of Process Fault Diagnosis -- Artificial Neural Networks -- Statistical Learning Theory and Kernel-Based Methods -- Tree-Based Methods -- Fault Diagnosis in Steady State Process Systems -- Dynamic Process Monitoring -- Process Monitoring Using Multiscale Methods.
520			_aAlgorithms for intelligent fault diagnosis of automated operations offer significant benefits to the manufacturing and process industries. Furthermore, machine learning methods enable such monitoring systems to handle nonlinearities and large volumes of data. This unique text/reference describes in detail the latest advances in Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: Reviews the application of machine learning to process monitoring and fault diagnosis Discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods Examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning Describes the use of spectral methods in process fault diagnosis This highly practical and clearly-structured work is an invaluable resource for all researchers and practitioners involved in process control, multivariate statistics and machine learning. Dr. Chris Aldrich is a Professor in the Department of Metallurgical and Minerals Engineering at Curtin University, Perth, Australia. Dr. Lidia Auret is a Lecturer in the Department of Process Engineering at Stellenbosch University, South Africa.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aAuret, Lidia. _eautor _9315261
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781447151845
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4471-5185-2 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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