Detecting mean changes in experience sampling data in real time: A comparison of univariate and multivariate statistical process control methods.

2021 ◽  
Author(s):  
Evelien Schat ◽  
Francis Tuerlinckx ◽  
Arnout C. Smit ◽  
Bart De Ketelaere ◽  
Eva Ceulemans
Keyword(s):  
Process Control ◽  
Real Time ◽  
Statistical Process Control ◽  
Experience Sampling ◽  
Multivariate Statistical Process Control ◽  
Control Methods ◽  
Multivariate Statistical ◽  
Statistical Process

scholarly journals Synchronization-Free Multivariate Statistical Process Control for Online Monitoring of Batch Process Evolution

2022 ◽  
Vol 1 ◽  
Author(s):  
Rodrigo Rocha de Oliveira ◽  
Anna de Juan
Keyword(s):  
Process Control ◽  
Real Time ◽  
Statistical Process Control ◽  
Batch Process ◽  
Operating Conditions ◽  
Batch Processes ◽  
Multivariate Statistical Process Control ◽  
Real Time Monitoring ◽  
Multivariate Statistical ◽  
Statistical Process

Synchronization of variable trajectories from batch process data is a delicate operation that can induce artifacts in the definition of multivariate statistical process control (MSPC) models for real-time monitoring of batch processes. The current paper introduces a new synchronization-free approach for online batch MSPC. This approach is based on the use of local MSPC models that cover a normal operating conditions (NOC) trajectory defined from principal component analysis (PCA) modeling of non-synchronized historical batches. The rationale behind is that, although non-synchronized NOC batches are used, an overall NOC trajectory with a consistent evolution pattern can be described, even if batch-to-batch natural delays and differences between process starting and end points exist. Afterwards, the local MSPC models are used to monitor the evolution of new batches and derive the related MSPC chart. During the real-time monitoring of a new batch, this strategy allows testing whether every new observation is following or not the NOC trajectory. For a NOC observation, an additional indication of the batch process progress is provided based on the identification of the local MSPC model that provides the lowest residuals. When an observation deviates from the NOC behavior, contribution plots based on the projection of the observation to the best local MSPC model identified in the last NOC observation are used to diagnose the variables related to the fault. This methodology is illustrated using two real examples of NIR-monitored batch processes: a fluidized bed drying process and a batch distillation of gasoline blends with ethanol.

Application of multivariate statistical process control to batch operations

2000 ◽  
Vol 24 (2-7) ◽  
pp. 291-296 ◽  
Author(s):  
B. Lennox ◽  
H.G. Hiden ◽  
G.A. Montague ◽  
G. Kornfeld ◽  
P.R. Goulding
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process

Making use of process tomography data for multivariate statistical process control

AIChE Journal ◽  
2010 ◽  
Vol 57 (9) ◽  
pp. 2360-2368 ◽  
Author(s):  
Bundit Boonkhao ◽  
Rui F. Li ◽  
Xue Z. Wang ◽  
Richard J. Tweedie ◽  
Ken Primrose
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Process Tomography ◽  
Tomography Data

scholarly journals Diagnosis of Out-of-control Signals in Multivariate Statistical Process Control Based on Bagging and Decision Tree

2017 ◽  
Vol 2 (2) ◽  
pp. 1 ◽  
Author(s):  
Jing Jiang ◽  
Hua-Ming Song
Keyword(s):  
Process Control ◽  
Decision Tree ◽  
Statistical Process Control ◽  
Ensemble Learning ◽  
Experimental Results ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Multiple Classifiers ◽  
Control Signals

In this paper, we propose an ensemble method based on bagging and decision tree to resolve the problem of diagnosing out-of-control signals in multivariate statistical process control. To classify the out-of-control signals, we obtain a series of classifiers through ensemble learning on decision tree. Then we will integrate the classification results of multiple classifiers to determine the final classification. The experimental results show that our method could improve the accuracy of classification and is superior to other methods in terms of diagnosing out-of-control signals in multivariate statistical process control.

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