Latent variable based key process variable identification and process monitoring for forging

2007 ◽  
Vol 26 (1) ◽  
pp. 53-61 ◽  
Author(s):  
Jihyun Kim ◽  
Qiang Huang ◽  
Jianjun Shi
Keyword(s):  
Process Monitoring ◽  
Latent Variable ◽  
Process Variable

Dynamic latent variable modeling for statistical process monitoring

2011 ◽  
Vol 44 (1) ◽  
pp. 12886-12891 ◽  
Author(s):  
Gang Li ◽  
Baosheng Liu ◽  
S. Joe Qin ◽  
Donghua Zhou
Keyword(s):  
Process Monitoring ◽  
Latent Variable ◽  
Latent Variable Modeling ◽  
Statistical Process ◽  
Statistical Process Monitoring

Combining fundamental knowledge and latent variable techniques to transfer process monitoring models between plants

2012 ◽  
Vol 116 ◽  
pp. 67-77 ◽  
Author(s):  
Emanuele Tomba ◽  
Pierantonio Facco ◽  
Fabrizio Bezzo ◽  
Salvador García-Muñoz ◽  
Massimiliano Barolo
Keyword(s):  
Process Monitoring ◽  
Transfer Process ◽  
Latent Variable ◽  
Fundamental Knowledge

scholarly journals A Process Monitoring Method Based on Dynamic Autoregressive Latent Variable Model and Its Application in the Sintering Process of Ternary Cathode Materials

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 229 ◽  
Author(s):  
Ning Chen ◽  
Fuhai Hu ◽  
Jiayao Chen ◽  
Zhiwen Chen ◽  
Weihua Gui ◽  
...  
Keyword(s):  
Process Monitoring ◽  
Cathode Materials ◽  
Latent Variable ◽  
Time Lag ◽  
Latent Variable Model ◽  
Sintering Process ◽  
Process Data ◽  
Variable Model ◽  
Monitoring Method ◽  
Variable Time

Due to the ubiquitous dynamics of industrial processes, the variable time lag raises great challenge to the high-precision industrial process monitoring. To this end, a process monitoring method based on the dynamic autoregressive latent variable model is proposed in this paper. First, from the perspective of process data, a dynamic autoregressive latent variable model (DALM) with process variables as input and quality variables as output is constructed to adapt to the variable time lag characteristic. In addition, a fusion Bayesian filtering, smoothing and expectation maximization algorithm is used to identify model parameters. Then, the process monitoring method based on DALM is constructed, in which the process data are filtered online to obtain the latent space distribution of the current state, and T2 statistics are constructed. Finally, by comparing with an existing method, the feasibility and effectiveness of the proposed method is tested on the sintering process of ternary cathode materials. Detailed comparisons show the superiority of the proposed method.

Autoregressive Dynamic Latent Variable Models for Process Monitoring

2017 ◽  
Vol 25 (1) ◽  
pp. 366-373 ◽  
Author(s):  
Le Zhou ◽  
Gang Li ◽  
Zhihuan Song ◽  
S. Joe Qin
Keyword(s):  
Process Monitoring ◽  
Latent Variable ◽  
Latent Variable Models
Sign in / Sign up

Export Citation Format

Share Document