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 …
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 …
Complex probabilistic slow feature extraction with applications in process data analytics
Today, in modern industrial processes, thousands of correlated process variables are
measured and stored. Dimension reduction techniques are often employed to construct …
measured and stored. Dimension reduction techniques are often employed to construct …
[HTML][HTML] Physics-informed probabilistic slow feature analysis
This paper presents a novel approach called physics-informed probabilistic slow feature
analysis. The probabilistic slow feature analysis method has been employed to extract …
analysis. The probabilistic slow feature analysis method has been employed to extract …
A novel semi-supervised robust learning framework for dynamic generative latent variable models and its application to industrial virtual metrology
Among a variety of virtual metrology models, dynamic generative latent variable models
(DGLVMs) have proven to be an effective tool, owing to outstanding advantages in dealing …
(DGLVMs) have proven to be an effective tool, owing to outstanding advantages in dealing …
Robust multi-mode probabilistic slow feature analysis with application to fault detection
This paper proposes a robust multi-mode dynamic data-driven model to identify complex
industrial processes and study its application in detecting incipient faults. To model the …
industrial processes and study its application in detecting incipient faults. To model the …
Generalized grouped contributions for hierarchical fault diagnosis with group Lasso
In process industries, it is necessary to conduct fault diagnosis after abnormality is found,
with the aim to identify root cause variables and further provide instructive information for …
with the aim to identify root cause variables and further provide instructive information for …
Semi‐supervised dynamic latent variable modeling: I/O probabilistic slow feature analysis approach
Modeling of high dimensional dynamic data is a challenging task. The high dimensionality
problem in process data is usually accounted for using latent variable models. Probabilistic …
problem in process data is usually accounted for using latent variable models. Probabilistic …
Sparse Robust Dynamic Feature Extraction using Bayesian Inference
Datasets of large-scale industrial processes are often high-dimensional and are
characterized by outliers. Probabilistic latent variable models are effective for modeling such …
characterized by outliers. Probabilistic latent variable models are effective for modeling such …
Data-driven modeling and operation optimization with inherent feature extraction for complex industrial processes
S Li, Y Zheng, S Li, M Huang - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In response to the tenets of Industry 4.0, operation optimization in industrial processes has
become a significant research topic. However, the uncertainties prevailing in the process …
become a significant research topic. However, the uncertainties prevailing in the process …