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008			210829s2022 sz | s |||| 0|eng d
020			_a9783030838195
024	7		_a10.1007/978-3-030-83819-5 _2doi
040			_aTR-AnTOB _beng _erda _cTR-AnTOB
041			_aeng
050		4	_aQ325.5
072		7	_aTGP _2bicssc
072		7	_aTEC020000 _2bisacsh
072		7	_aTGP _2thema
090			_aQ325.5EBK
245	1	0	_aControl Charts and Machine Learning for Anomaly Detection in Manufacturing _h[electronic resource] / _cedited by Kim Phuc Tran.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Series in Reliability Engineering, _x2196-999X
505	0		_aAnomaly Detection in Manufacturing -- EWMA Time-Between-Events-and-Amplitude Control Charts for Correlated Data -- An Adaptive Exponentially Weighted Moving Average Chart for the Ratio of Two Normal Variables -- On the Performance of CUSUM t Chart in the Presence of Measurement Errors -- The Effect of Autocorrelation on the Shewhart Control Chart for the Ratio of Two Normal Variables -- LSTM Autoencoder Control Chart for Multivariate Time Series Data -- Real-Time Production Monitoring Approach for Smart Manufacturing with Artificial Intelligence Techniques -- Anomaly Detection in Graph with Machine Learning -- Profile Control Charts Based on Support Vector Data Description -- An Anomaly Detection Approach Based on the Combination of LSTM Autoencoder and Isolation Forest for Multivariate Time Series Data.
520			_aThis book introduces the latest research on advanced control charts and new machine learning approaches to detect abnormalities in the smart manufacturing process. By approaching anomaly detection using both statistics and machine learning, the book promotes interdisciplinary cooperation between the research communities, to jointly develop new anomaly detection approaches that are more suitable for the 4.0 Industrial Revolution. The book provides ready-to-use algorithms and parameter sheets, enabling readers to design advanced control charts and machine learning-based approaches for anomaly detection in manufacturing. Case studies are introduced in each chapter to help practitioners easily apply these tools to real-world manufacturing processes. The book is of interest to researchers, industrial experts, and postgraduate students in the fields of industrial engineering, automation, statistical learning, and manufacturing industries.
650		0	_aManufactures.
650		0	_aIndustrial Management.
650		0	_aMachine learning.
650		0	_aStatistics .
650		0	_aCooperating objects (Computer systems).
650	1	4	_aMachines, Tools, Processes.
650	2	4	_aIndustrial Management.
650	2	4	_aMachine Learning.
650	2	4	_aApplied Statistics.
650	2	4	_aCyber-Physical Systems.
653		0	_aArtificial intelligence -- Industrial applications
653		0	_aMachine learning
700	1		_aTran, Kim Phuc. _eeditor. _0(orcid)0000-0002-6005-1497 _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
830		0	_aSpringer Series in Reliability Engineering, _x2196-999X
856	4	0	_uhttps://doi.org/10.1007/978-3-030-83819-5 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK