• Corpus ID: 239024406

Hybrid variable monitoring: An unsupervised process monitoring framework

@article{Wang2021HybridVM,
  title={Hybrid variable monitoring: An unsupervised process monitoring framework},
  author={Min Wang and Donghua Zhou and Maoyin Chen},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.09704}
}
Traditional process monitoring methods, such as PCA, PLS, ICA, MD et al., are strongly dependent on continuous variables because most of them inevitably involve Euclidean or Mahalanobis distance. With industrial processes becoming more and more complex and integrated, binary variables also appear in monitoring variables besides continuous variables, which makes process monitoring more challenging. The aforementioned traditional approaches are incompetent to mine the information of binary… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 42 REFERENCES
Nonlinear dynamic process monitoring based on dynamic kernel PCA
Abstract Nonlinear dynamic process monitoring based on dynamic kernel principal component analysis (DKPCA) is proposed. The kernel functions used in kernel PCA (KPCA) are profitable for capturing
Anomaly detection in the fan system of a thermal power plant monitored by continuous and two-valued variables
TLDR
A novel mixed hidden naive Bayesian model (MHNBM) is developed within the probability framework, and can efficiently enhance the detection performance through combining the two-valued variables.
Statistical process monitoring with independent component analysis
In this paper we propose a new statistical method for process monitoring that uses independent component analysis (ICA). ICA is a recently developed method in which the goal is to decompose observed
A Common and Individual Feature Extraction-Based Multimode Process Monitoring Method With Application to the Finishing Mill Process
TLDR
A common and individual (CnI) feature extraction-based process monitoring (PM) method for tracking the operating performance and product quality of processes with multiple operating modes that can be preferable to detect and identify different faults in the multimode FMP.
MoniNet With Concurrent Analytics of Temporal and Spatial Information for Fault Detection in Industrial Processes.
TLDR
The illustration results show that the proposed cascaded monitoring network (MoniNet) method can effectively detect process anomalies by concurrent analytics of temporal and spatial information.
Deep Principal Component Analysis Based on Layerwise Feature Extraction and Its Application to Nonlinear Process Monitoring
TLDR
A hierarchical statistical model structure to extract multilayer data features, including both the linear and nonlinear principal components, is designed, motivated by the deep learning strategy, to reduce the computation complexity in nonlinear feature extraction.
Multimode Process Monitoring Based on Switching Autoregressive Dynamic Latent Variable Model
TLDR
A set of switching ARDLV models are proposed in the probabilistic framework, which extends the original single model to its multimode form and a hierarchical fault detection method is developed for process monitoring in the multimode processes.
Recursive PCA for adaptive process monitoring
TLDR
A complete adaptive monitoring algorithm that addresses the issues of missing values and outlines is presented and is applied to a rapid thermal annealing process in semiconductor processing for adaptive monitoring.
Disturbance detection and isolation by dynamic principal component analysis
TLDR
This paper uses a well-known ‘time lag shift’ method to include dynamic behavior in the PCA model and demonstrates the effectiveness of the proposed methodology on the Tennessee Eastman process simulation.
Multivariate statistical monitoring of process operating performance
Process computers routinely collect hundreds to thousands of pieces of data from a multitude of plant sensors every few seconds. This has caused a “data overload” and due to the lack of appropriate
...
1
2
3
4
5
...