%0 Book Section %1 MaierSchriegelNiggemann17p473 %A Maier, Alexander %A Schriegel, Sebastian %A Niggemann, Oliver %B Industrial Internet of Things %C Cham %D 2017 %E Jeschke, Sabina %E Brecher, Christian %E Song, Houbing %E Rawat, Danda B. %I Springer %K 01821 springer paper embedded ai factory data pattern recognition admin optimize learn zzz.big zzz.i40 %P 473--485 %R 10.1007/978-3-319-42559-7_18 %T Big Data and Machine Learning for the Smart Factory---Solutions for Condition Monitoring, Diagnosis and Optimization %X The increasing heterogeneity of automation systems coupled with the demand of more flexibility leads to a rising complexity of production plants. For this reason, production plants are becoming highly error-prone, and error detection is getting more difficult. Additionally, a great amount of expert knowledge is required to optimize production plants. Although machine learning approaches help to cope with these problems, these approaches are reaching their limits fast. As a result, approaches using Big Data are getting more popular, which help in finding relationships within the data, reliably detect anomalies (in advance), and allow an automatic optimization of the system. This contribution presents some approaches in the context of Big Data analyses in discrete continuous and hybrid production plants. This enables services in smart factories such as condition monitoring diagnosis and optimization. The contribution is rounded off with the presentation of practical examples of how the smart services can be applied. %@ 978-3-319-42558-0

@incollection{MaierSchriegelNiggemann17p473, abstract = {The increasing heterogeneity of automation systems coupled with the demand of more flexibility leads to a rising complexity of production plants. For this reason, production plants are becoming highly error-prone, and error detection is getting more difficult. Additionally, a great amount of expert knowledge is required to optimize production plants. Although machine learning approaches help to cope with these problems, these approaches are reaching their limits fast. As a result, approaches using Big Data are getting more popular, which help in finding relationships within the data, reliably detect anomalies (in advance), and allow an automatic optimization of the system. This contribution presents some approaches in the context of Big Data analyses in discrete continuous and hybrid production plants. This enables services in smart factories such as condition monitoring diagnosis and optimization. The contribution is rounded off with the presentation of practical examples of how the smart services can be applied.}, added-at = {2017-01-01T15:27:30.000+0100}, address = {Cham}, author = {Maier, Alexander and Schriegel, Sebastian and Niggemann, Oliver}, biburl = {https://www.bibsonomy.org/bibtex/26019c019aaf056e944ed916e42df52df/flint63}, booktitle = {Industrial Internet of Things}, crossref = {JeschkeBrecherEtAl2017}, doi = {10.1007/978-3-319-42559-7_18}, editor = {Jeschke, Sabina and Brecher, Christian and Song, Houbing and Rawat, Danda B.}, file = {SpringerLink:2017/MaierSchriegelNiggemann17p473.pdf:PDF}, groups = {public}, interhash = {ea4a769c29f5a0195ed8e52d5221755d}, intrahash = {6019c019aaf056e944ed916e42df52df}, isbn = {978-3-319-42558-0}, issn = {2365-4139}, keywords = {01821 springer paper embedded ai factory data pattern recognition admin optimize learn zzz.big zzz.i40}, pages = {473--485}, publisher = {Springer}, series = {Springer Series in Wireless Technology}, timestamp = {2018-04-16T12:04:54.000+0200}, title = {Big Data and Machine Learning for the Smart Factory---Solutions for Condition Monitoring, Diagnosis and Optimization}, username = {flint63}, year = 2017 }