Data and knowledge mining with big data towards smart production
Driven by the innovative improvement of information and communication technologies
(ICTs) and their applications into manufacturing industry, the big data era in manufacturing is …
(ICTs) and their applications into manufacturing industry, the big data era in manufacturing is …
A review of machine learning kernel methods in statistical process monitoring
The complexity of modern problems turns increasingly larger in industrial environments, so
the classical process monitoring techniques have to adapt to deal with those problems. This …
the classical process monitoring techniques have to adapt to deal with those problems. This …
[HTML][HTML] New statistical and machine learning based control charts with variable parameters for monitoring generalized linear model profiles
In this research, we develop three statistical based control charts: the Hotelling's T 2,
MEWMA (multivariate exponentially weighted moving average), and LRT (likelihood ratio …
MEWMA (multivariate exponentially weighted moving average), and LRT (likelihood ratio …
Machine learning in production–potentials, challenges and exemplary applications
Recent trends like autonomous driving, natural language processing, service robotics or
Industry 4.0 are mainly based on the tremendous progress made in the field of machine …
Industry 4.0 are mainly based on the tremendous progress made in the field of machine …
A survey of control-chart pattern-recognition literature (1991–2010) based on a new conceptual classification scheme
Control Chart Pattern Recognition (CCPR) is a critical task in Statistical Process Control
(SPC). Abnormal patterns exhibited in control charts can be associated with certain …
(SPC). Abnormal patterns exhibited in control charts can be associated with certain …
Application of machine learning in statistical process control charts: A survey and perspective
Over the past decades, control charts, one of the essential tools in Statistical Process Control
(SPC), have been widely implemented in manufacturing industries as an effective approach …
(SPC), have been widely implemented in manufacturing industries as an effective approach …
Monitoring linear profiles using Artificial Neural Networks with run rules
In some applications, a relation between a response variable and one or more explanatory
variables (referred as a “profile”) characterizes the quality of a process. Profile monitoring is …
variables (referred as a “profile”) characterizes the quality of a process. Profile monitoring is …
Support vector machine in statistical process monitoring: a methodological and analytical review
S Cuentas, R Peñabaena-Niebles, E Garcia - The International Journal of …, 2017 - Springer
In statistical process monitoring, data mining algorithms are applied for control chart pattern
recognition (CCPR) not only to detect but also to identify abnormal patterns associated with …
recognition (CCPR) not only to detect but also to identify abnormal patterns associated with …
An SVM-GA based monitoring system for pattern recognition of autocorrelated processes
S Cuentas, E García, R Peñabaena-Niebles - Soft Computing, 2022 - Springer
Statistical process control (SPC) has proven to be an effective tool for measuring, controlling,
and improving a process through the application of statistical procedures. The most valuable …
and improving a process through the application of statistical procedures. The most valuable …
Using a time delay neural network approach to diagnose the out-of-control signals for a multivariate normal process with variance shifts
YE Shao, SC Lin - Mathematics, 2019 - mdpi.com
With the rapid development of advanced sensor technologies, it has become popular to
monitor multiple quality variables for a manufacturing process. Consequently, multivariate …
monitor multiple quality variables for a manufacturing process. Consequently, multivariate …