[HTML][HTML] Data analytics and machine learning for smart process manufacturing: Recent advances and perspectives in the big data era

C Shang, F You - Engineering, 2019 - Elsevier
Safe, efficient, and sustainable operations and control are primary objectives in industrial
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

A Thebelt, J Wiebe, J Kronqvist, C Tsay… - Chemical Engineering …, 2022 - Elsevier
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 …

Advances and opportunities in machine learning for process data analytics

SJ Qin, LH Chiang - Computers & Chemical Engineering, 2019 - Elsevier
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 …

Fault detection for nonlinear dynamic systems with consideration of modeling errors: A data-driven approach

H Chen, L Li, C Shang, B Huang - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …

An extended Tennessee Eastman simulation dataset for fault-detection and decision support systems

C Reinartz, M Kulahci, O Ravn - Computers & chemical engineering, 2021 - Elsevier
Abstract The Tennessee Eastman Process (TEP) is a frequently used benchmark in
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

SJ Qin, Y Dong, Q Zhu, J Wang, Q Liu - Annual Reviews in Control, 2020 - Elsevier
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 …

[HTML][HTML] Cyber–physical production systems for data-driven, decentralized, and secure manufacturing—A perspective

M Suvarna, KS Yap, W Yang, J Li, YT Ng, X Wang - Engineering, 2021 - Elsevier
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 …

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 …

Data-driven process monitoring and fault diagnosis: A comprehensive survey

A Melo, MM Câmara, JC Pinto - Processes, 2024 - mdpi.com
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 …

Efficient dynamic latent variable analysis for high-dimensional time series data

Y Dong, Y Liu, SJ Qin - IEEE Transactions on Industrial …, 2019 - ieeexplore.ieee.org
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 …