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008			130616s2013 xxk| s |||| 0|eng d
020			_a9781447151852 _9978-1-4471-5185-2
024	7		_a10.1007/978-1-4471-5185-2 _2doi
050		4	_aQ334-342
050		4	_aTJ210.2-211.495
072		7	_aUYQ _2bicssc
072		7	_aTJFM1 _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_aAldrich, Chris. _eauthor.
245	1	0	_aUnsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods _h[electronic resource] / _cby Chris Aldrich, Lidia Auret.
264		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2013.
300			_aXIX, 374 p. 208 illus., 151 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		_aAdvances in Computer Vision and Pattern Recognition, _x2191-6586
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.
650		0	_aComputer science.
650		0	_aArtificial intelligence.
650	1	4	_aComputer Science.
650	2	4	_aArtificial Intelligence (incl. Robotics).
700	1		_aAuret, Lidia. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781447151845
830		0	_aAdvances in Computer Vision and Pattern Recognition, _x2191-6586
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4471-5185-2
912			_aZDB-2-SCS
942			_2Dewey Decimal Classification _ceBooks
999			_c43765 _d43765