Understanding big data analytics for manufacturing processes: insights from literature review and multiple case studies
Today, we are undoubtedly in the era of data. Big Data Analytics (BDA) is no longer a
perspective for all level of the organization. This is of special interest in the manufacturing …
perspective for all level of the organization. This is of special interest in the manufacturing …
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 …
How generative AI models such as ChatGPT can be (mis) used in SPC practice, education, and research? An exploratory study
Abstract Generative Artificial Intelligence (AI) models such as OpenAI's ChatGPT have the
potential to revolutionize Statistical Process Control (SPC) practice, learning, and research …
potential to revolutionize Statistical Process Control (SPC) practice, learning, and research …
[HTML][HTML] Industrial process monitoring in the big data/industry 4.0 era: From detection, to diagnosis, to prognosis
MS Reis, G Gins - Processes, 2017 - mdpi.com
We provide a critical outlook of the evolution of Industrial Process Monitoring (IPM) since its
introduction almost 100 years ago. Several evolution trends that have been structuring IPM …
introduction almost 100 years ago. Several evolution trends that have been structuring IPM …
A novel orthogonal self-attentive variational autoencoder method for interpretable chemical process fault detection and identification
X Bi, J Zhao - Process Safety and Environmental Protection, 2021 - Elsevier
Industrial processes are becoming increasingly large and complex, thus introducing
potential safety risks and requiring an effective approach to maintain safe production …
potential safety risks and requiring an effective approach to maintain safe production …
[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 …
Meta deep learning based rotating machinery health prognostics toward few-shot prognostics
Data-driven health prognostic is attracting more and more attention to machinery prognostic
and health management. It enables machinery to realize predictive maintenance and rarely …
and health management. It enables machinery to realize predictive maintenance and rarely …
[HTML][HTML] Automatic anomaly detection on in-production manufacturing machines using statistical learning methods
Anomaly detection is becoming increasingly important to enhance reliability and resiliency
in the Industry 4.0 framework. In this work, we investigate different methods for anomaly …
in the Industry 4.0 framework. In this work, we investigate different methods for anomaly …
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 …
Recent developments of control charts, identification of big data sources and future trends of current research
RG Aykroyd, V Leiva, F Ruggeri - Technological Forecasting and Social …, 2019 - Elsevier
Control charts are one of the principal tools to monitor dynamic processes with the aim of
rapid identification of changes in the behaviour of these processes. Such changes are …
rapid identification of changes in the behaviour of these processes. Such changes are …