001442196 000__ 03659cam\a2200493\i\4500
001442196 001__ 1442196
001442196 003__ OCoLC
001442196 005__ 20230310003317.0
001442196 006__ m\\\\\o\\d\\\\\\\\
001442196 007__ cr\cn\nnnunnun
001442196 008__ 210831s2022\\\\sz\a\\\\o\\\\\000\0\eng\d
001442196 020__ $$a9783030838195$$q(electronic bk.)
001442196 020__ $$a3030838196$$q(electronic bk.)
001442196 020__ $$z9783030838188$$q(print)
001442196 0247_ $$a10.1007/978-3-030-83819-5$$2doi
001442196 035__ $$aSP(OCoLC)1266188345
001442196 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dOCLCO$$dN$T$$dOCLCF$$dOCLCQ$$dOCLCO$$dOCLCQ
001442196 049__ $$aISEA
001442196 050_4 $$aQ325.5
001442196 08204 $$a006.3/1$$223
001442196 24500 $$aControl charts and machine learning for anomaly detection in manufacturing /$$cKim Phuc Tran, editors.
001442196 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2022]
001442196 300__ $$a1 online resource (vi, 269 pages) :$$billustrations (some color)
001442196 336__ $$atext$$btxt$$2rdacontent
001442196 337__ $$acomputer$$bc$$2rdamedia
001442196 338__ $$aonline resource$$bcr$$2rdacarrier
001442196 4901_ $$aSpringer series in reliability engineering,$$x2196-999X
001442196 5050_ $$aAnomaly Detection in Manufacturing -- EWMA Time-Between-Events-and-Amplitude Control Charts for Correlated Data -- An Adaptive Exponentially Weighted Moving Average Chart for the Ratio of Two Normal Variables -- On the Performance of CUSUM t Chart in the Presence of Measurement Errors -- The Effect of Autocorrelation on the Shewhart Control Chart for the Ratio of Two Normal Variables -- LSTM Autoencoder Control Chart for Multivariate Time Series Data -- Real-Time Production Monitoring Approach for Smart Manufacturing with Artificial Intelligence Techniques -- Anomaly Detection in Graph with Machine Learning -- Profile Control Charts Based on Support Vector Data Description -- An Anomaly Detection Approach Based on the Combination of LSTM Autoencoder and Isolation Forest for Multivariate Time Series Data.
001442196 506__ $$aAccess limited to authorized users.
001442196 520__ $$aThis book introduces the latest research on advanced control charts and new machine learning approaches to detect abnormalities in the smart manufacturing process. By approaching anomaly detection using both statistics and machine learning, the book promotes interdisciplinary cooperation between the research communities, to jointly develop new anomaly detection approaches that are more suitable for the 4.0 Industrial Revolution. The book provides ready-to-use algorithms and parameter sheets, enabling readers to design advanced control charts and machine learning-based approaches for anomaly detection in manufacturing. Case studies are introduced in each chapter to help practitioners easily apply these tools to real-world manufacturing processes. The book is of interest to researchers, industrial experts, and postgraduate students in the fields of industrial engineering, automation, statistical learning, and manufacturing industries.
001442196 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 31, 2021).
001442196 650_0 $$aMachine learning.
001442196 650_0 $$aArtificial intelligence$$xIndustrial applications.
001442196 650_6 $$aApprentissage automatique.
001442196 650_6 $$aIntelligence artificielle$$xApplications industrielles.
001442196 655_0 $$aElectronic books.
001442196 7001_ $$aTran, Kim Phuc,$$eeditor$$0(orcid)0000-0002-6005-1497$$1https://orcid.org/0000-0002-6005-1497
001442196 830_0 $$aSpringer series in reliability engineering,$$x2196-999X
001442196 852__ $$bebk
001442196 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-83819-5$$zOnline Access$$91397441.1
001442196 909CO $$ooai:library.usi.edu:1442196$$pGLOBAL_SET
001442196 980__ $$aBIB
001442196 980__ $$aEBOOK
001442196 982__ $$aEbook
001442196 983__ $$aOnline
001442196 994__ $$a92$$bISE