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008			230318s2022 sz | s |||| 0|eng d
020			_a9783030838195
024	7		_a10.1007/978-3-030-83819-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2022 EB
245	0	0	_aControl Charts and Machine Learning for Anomaly Detection in Manufacturing _cedited by Kim Phuc Tran
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (VI, 269 páginas) _b67 ilustraciones, 38 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSpringer_Engineering_2022
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _aInteligencia artificial _xAplicaciones industriales _9413115
776	0	8	_iPrinted edition: _z9783030838188
776	0	8	_iPrinted edition: _z9783030838201
776	0	8	_iPrinted edition: _z9783030838218
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-83819-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eIG _zSI