[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 …
Slow down to go better: A survey on slow feature analysis
Temporal data contain a wealth of valuable information, playing an essential role in various
machine-learning tasks. Slow feature analysis (SFA), one of the most classic temporal …
machine-learning tasks. Slow feature analysis (SFA), one of the most classic temporal …
Retrospective comparison of several typical linear dynamic latent variable models for industrial process monitoring
Process dynamic behaviors resulting from closed-loop control and the inherence of
processes are ubiquitous in industrial processes and bring a considerable challenge for …
processes are ubiquitous in industrial processes and bring a considerable challenge for …
Process data analytics via probabilistic latent variable models: A tutorial review
Z Ge - Industrial & Engineering Chemistry Research, 2018 - ACS Publications
Dimensionality reduction is important for the high-dimensional nature of data in the process
industry, which has made latent variable modeling methods popular in recent years. By …
industry, which has made latent variable modeling methods popular in recent years. By …
Fault-tolerant soft sensors for dynamic systems
H Chen, B Huang - IEEE Transactions on Control Systems …, 2023 - ieeexplore.ieee.org
Unpredicted faults occurring in automation systems deteriorate the performance of soft
sensors and may even lead to incorrect results. To address the problem, this study develops …
sensors and may even lead to incorrect results. To address the problem, this study develops …
Supervised variational autoencoders for soft sensor modeling with missing data
R Xie, NM Jan, K Hao, L Chen… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Autoencoder (AE) is a deep neural network that has been widely utilized in process industry
owing to its superior abilities of feature extraction and data reconstruction. Recently …
owing to its superior abilities of feature extraction and data reconstruction. Recently …
Dual attention-based encoder–decoder: A customized sequence-to-sequence learning for soft sensor development
L Feng, C Zhao, Y Sun - IEEE Transactions on Neural …, 2020 - ieeexplore.ieee.org
Soft sensor techniques have been applied to predict the hard-to-measure quality variables
based on the easy-to-measure process variables in industry scenarios. Since the products …
based on the easy-to-measure process variables in industry scenarios. Since the products …
Slow-varying dynamics-assisted temporal capsule network for machinery remaining useful life estimation
Capsule network (CapsNet) acts as a promising alternative to the typical convolutional
neural network, which is the dominant network to develop the remaining useful life (RUL) …
neural network, which is the dominant network to develop the remaining useful life (RUL) …
A generalized probabilistic monitoring model with both random and sequential data
Many multivariate statistical analysis methods and their corresponding probabilistic
counterparts have been adopted to develop process monitoring models in recent decades …
counterparts have been adopted to develop process monitoring models in recent decades …
Data-driven batch-end quality modeling and monitoring based on optimized sparse partial least squares
Batch-end quality modeling is used to predict the quality by using batch measurements and
generally involves a large number of predictor variables. However, not all of the variables …
generally involves a large number of predictor variables. However, not all of the variables …