Dynamic latent variable modeling for statistical process monitoring
نویسندگان
چکیده
Dynamic principal component analysis (DPCA) has been widely used in the monitoring of dynamic multivariate processes. In traditional DPCA, the dynamic relationship between process variables are implicit and hard to interpret. To extract explicit latent factors that are dynamically correlated, a new dynamic latent variable model is proposed. The new structure can improve modeling of dynamic data and enhance the process monitoring performance. Fault detection indices are developed based on the proposed model. A case study is given to illustrate the effectiveness of the proposed new dynamic factor model.
منابع مشابه
Process Modeling by Bayesian Latent Variable Regression
Process Modeling by Bayesian Latent Variable Regression Mohamed N. Nounou, Bhavik R. Bakshi Prem K. Goel, Xiaotong Shen Department of Chemical Engineering Department of Statistics The Ohio State University, Columbus, OH 43210, USA Abstract Large quantities of measured data are being routinely collected in a variety of industries and used for extracting linear models for tasks such as, process c...
متن کاملLatent Variable-based Key Process Variable Identification and Process Monitoring for Forging
Monitoring for Forging Jihyun Kim, Qiang Huang, and Jianjun Shi Department of Industrial and Operations Engineering The University of Michigan Ann Arbor, MI 48109 Department of Industrial and Management Systems Engineering University of South Florida Tampa, FL 33620 Abstract The fast developing sensing technology has significantly increasing the accessibility of processing condition information...
متن کاملA risk adjusted self-starting Bernoulli CUSUM control chart with dynamic probability control limits
Usually, in monitoring schemes the nominal value of the process parameter is assumed known. However, this assumption is violated owing to costly sampling and lack of data particularly in healthcare systems. On the other hand, applying a fixed control limit for the risk-adjusted Bernoulli chart causes to a variable in-control average run length performance for patient populations with dissimilar...
متن کاملDynamic texture modeling and synthesis using multi-kernel Gaussian process dynamic model
Dynamic texture (DT) widely exists in various social video media. Therefore, DT modeling and synthesis plays an important role in social media analyzing and processing. In this paper, we propose a Bayesian-based nonlinear dynamic texture modeling method for dynamic texture synthesis. To capture the non-stationary distribution of DT, we utilize the Gaussian process latent variable model for dime...
متن کاملModeling Sounds with Gaussian Modulation Cascade Processes
The processing of sensory input is intimately linked to its statistical structure, and this relationship can been used to derive computational models of sensory processing. For instance, one approach is to devise a model for the sensory data that captures the observed statistical structure through latent variables, and then to view sensory processing as inference under this model. This approach...
متن کامل