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 …
Semi-supervised data modeling and analytics in the process industry: Current research status and challenges
Z Ge - IFAC Journal of systems and control, 2021 - Elsevier
Semi-supervised data are quite common in the process industry, which has caught much
attention in recent years. The semi-supervised feature of process data not only has a great …
attention in recent years. The semi-supervised feature of process data not only has a great …
非平稳间歇过程数据解析与状态监控—回顾与展望
赵春晖， 余万科， 高福荣 - 自动化学报, 2020 - aas.net.cn
间歇过程作为制造业的重要生产方式之一, 其高效运行是智能制造的优先主题.
为了保障生产过程的高效运行, 面向间歇生产的过程数据解析与状态监控算法在最近三十年间 …
为了保障生产过程的高效运行, 面向间歇生产的过程数据解析与状态监控算法在最近三十年间 …
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 …
Semi-supervised deep dynamic probabilistic latent variable model for multimode process soft sensor application
L Yao, B Shen, L Cui, J Zheng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Nonlinear and multimode characteristics commonly appear in modern industrial process
data with increasing complexity and dynamics, which have brought challenges to soft sensor …
data with increasing complexity and dynamics, which have brought challenges to soft sensor …
Variational bayesian approach to nonstationary and oscillatory slow feature analysis with applications in soft sensing and process monitoring
Extraction of underlying patterns from measured variables is central to various data-driven
control applications, such as soft-sensor modeling, statistical process monitoring, and fault …
control applications, such as soft-sensor modeling, statistical process monitoring, and fault …
Virtual sensor modeling for nonlinear dynamic processes based on local weighted PSFA
Modern industrial processes are featured with complex dynamic, nonlinear, and noisy
characteristics. It is of great significance to apply the probabilistic latent variable models …
characteristics. It is of great significance to apply the probabilistic latent variable models …
Evolutionary optimization based pseudo labeling for semi-supervised soft sensor development of industrial processes
Data-based soft sensors have been widely applied in industrial processes for enabling
online prediction of difficult-to-measure variables. However, there exists a common …
online prediction of difficult-to-measure variables. However, there exists a common …
Dynamic system modelling and process monitoring based on long-term dependency slow feature analysis
X Gao, YAW Shardt - Journal of Process Control, 2021 - Elsevier
Modern industrial processes are large-scale, highly complex systems with many units and
equipment. The complex flow of mass and energy, as well as the compensation effects of …
equipment. The complex flow of mass and energy, as well as the compensation effects of …
A cloud–edge collaboration based quality-related hierarchical fault detection framework for large-scale manufacturing processes
X Zhang, L Ma, K Peng, C Zhang, MA Shahid - Expert Systems with …, 2024 - Elsevier
Against the backdrop of the new-generation intelligent manufacturing and Industrial Internet
of Things, manufacturing processes are evolving towards integration, large-scale …
of Things, manufacturing processes are evolving towards integration, large-scale …