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[HTML][HTML] Data analytics and machine learning for smart process manufacturing: Recent advances and perspectives in the big data era
Safe, efficient, and sustainable operations and control are primary objectives in industrial
manufacturing processes. State-of-the-art technologies heavily rely on human intervention …
manufacturing processes. State-of-the-art technologies heavily rely on human intervention …
[HTML][HTML] Maximizing information from chemical engineering data sets: Applications to machine learning
It is well-documented how artificial intelligence can have (and already is having) a big
impact on chemical engineering. But classical machine learning approaches may be weak …
impact on chemical engineering. But classical machine learning approaches may be weak …
Advances and opportunities in machine learning for process data analytics
In this paper we introduce the current thrust of development in machine learning and
artificial intelligence, fueled by advances in statistical learning theory over the last 20 years …
artificial intelligence, fueled by advances in statistical learning theory over the last 20 years …
Fault detection for nonlinear dynamic systems with consideration of modeling errors: A data-driven approach
This article is concerned with data-driven realization of fault detection (FD) for nonlinear
dynamic systems. In order to identify and parameterize nonlinear Hammerstein models …
dynamic systems. In order to identify and parameterize nonlinear Hammerstein models …
An extended Tennessee Eastman simulation dataset for fault-detection and decision support systems
Abstract The Tennessee Eastman Process (TEP) is a frequently used benchmark in
chemical engineering research. An extended simulator, published in 2015, enables a more …
chemical engineering research. An extended simulator, published in 2015, enables a more …
Bridging systems theory and data science: A unifying review of dynamic latent variable analytics and process monitoring
This paper is concerned with data science and analytics as applied to data from dynamic
systems for the purpose of monitoring, prediction, and inference. Collinearity is inevitable in …
systems for the purpose of monitoring, prediction, and inference. Collinearity is inevitable in …
[HTML][HTML] Cyber–physical production systems for data-driven, decentralized, and secure manufacturing—A perspective
With the concepts of Industry 4.0 and smart manufacturing gaining popularity, there is a
growing notion that conventional manufacturing will witness a transition toward a new …
growing notion that conventional manufacturing will witness a transition toward a new …
A novel quality-related incipient fault detection method based on canonical variate analysis and Kullback–Leibler divergence for large-scale industrial processes
J Dong, L Jiang, C Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Quality-related fault detection is an effective way to ensure the stability of product quality and
the safety of industrial processes. Quality abnormality is often caused by incipient faults …
the safety of industrial processes. Quality abnormality is often caused by incipient faults …
Data-driven process monitoring and fault diagnosis: A comprehensive survey
This paper presents a comprehensive review of the historical development, the current state
of the art, and prospects of data-driven approaches for industrial process monitoring. The …
of the art, and prospects of data-driven approaches for industrial process monitoring. The …
Efficient dynamic latent variable analysis for high-dimensional time series data
Dynamic-inner canonical correlation analysis (DiCCA) extracts dynamic latent variables
from high-dimensional time series data with a descending order of predictability in terms of …
from high-dimensional time series data with a descending order of predictability in terms of …