Journal ArticleDOI
Dynamic latent variable modeling for statistical process monitoring
Reads0
Chats0
TLDR
A new dynamic latent variable model is proposed that can improve modeling of dynamic data and enhance the process monitoring performance in dynamic multivariate processes.About:
This article is published in IFAC Proceedings Volumes.The article was published on 2011-01-01. It has received 33 citations till now. The article focuses on the topics: Latent variable model & Latent class model.read more
Citations
More filters
Journal ArticleDOI
A novel dynamic PCA algorithm for dynamic data modeling and process monitoring
TL;DR: A novel dynamic PCA (DiPCA) algorithm is proposed to extract explicitly a set of dynamic latent variables with which to capture the most dynamic variations in the data.
Journal ArticleDOI
Quality Relevant Data-Driven Modeling and Monitoring of Multivariate Dynamic Processes: The Dynamic T-PLS Approach
TL;DR: A dynamic PLS algorithm is proposed in this paper for dynamic process modeling, which captures the dynamic correlation between the measurement block and quality data block, and the effectiveness of dynamic T-PLS models and the corresponding fault detection methods is shown.
Journal ArticleDOI
Bridging systems theory and data science: A unifying review of dynamic latent variable analytics and process monitoring
TL;DR: A new dimension reduction expression of state space framework is presented to unify dynamic latent variable analytics for process data, dynamic factor models for econometrics, subspace identification of multivariate dynamic systems, and machine learning algorithms for dynamic feature analysis.
Journal ArticleDOI
The current status of temporal network analysis for clinical science: Considerations as the paradigm shifts?
TL;DR: Context is provided for how temporal networks models that examine directionality between symptoms over time are applied to clinically-relevant longitudinal data, emphasizing how temporal Networks best approximate network theory.
Journal ArticleDOI
Efficient Dynamic Latent Variable Analysis for High-Dimensional Time Series Data
TL;DR: Numerically efficient algorithms for DiCCA are developed to extract dynamic latent components from high-dimensional time series data and the dynamic model compaction of the extracted latent scores using autoregressive integrated moving average (ARIMA) models is improved.
References
More filters
Book
System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Journal ArticleDOI
PLS regression methods
TL;DR: In this paper, the mathematical and statistical structure of PLS regression is developed and the PLS decomposition of the data matrices involved in model building is analyzed. But the PLP regression algorithm can be interpreted in a model building setting.
Journal ArticleDOI
Statistical process monitoring: basics and beyond
TL;DR: It is demonstrated that the reconstruction-based framework provides a convenient way for fault analysis, including fault detectability, reconstructability and identifiability conditions, resolving many theoretical issues in process monitoring.
Journal ArticleDOI
Disturbance detection and isolation by dynamic principal component analysis
TL;DR: 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.
Journal ArticleDOI
Statistical Process Control of Multivariate Processes
TL;DR: An overview of multivariate statistical methods use for the statistical process control of both continuous and batch multivariate processes and examples are provided of their use for analysing the operations of a mineral processing plant, for on-line monitoring and fault diagnosis of a continuous polymerization process and for the on- line monitoring of an industrial batch polymerization reactor.