System Engineering and Productivity

System Engineering and Productivity

A Machine Learning-based Control Chart for Monitoring the Dispersion of High-dimensional Data Streams in Phase II

Articles in Press

Document Type : Research Paper

Authors
Mir Milad Ghazvini 1
Ali Salmasnia 2
1 Ph.D. Student, Faculty of Industrial Engineering, University of Eyvanekey, Eyvanekey, Iran
2 Corresponding: Associate Professor, Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
10.22034/sep.2025.2069790.1387
Abstract
This fully connected neural network-based control chart is developed for monitoring the dispersion of multivariate processes with two important features: (1) the ability to be used for monitoring the dispersion of processes with high-dimensional data streams, and (2) the absence of the need to establish restrictive statistical assumptions, such as normality of the quality characteristics under study and the independence of observations in the samples taken. An important challenge that we usually face in training neural networks is overfitting or generalization failure. In this study, two tools of dropout layer and weight regularization are used in network design to face the mentioned challenge. In addition, in order to better train the neural network and unlike most control charts based on learning tools that use a two-class pattern of zero and one as target values, in this study the target values are determined based on the size and number of shifted components, so that as the shift size and the number of shifted components increase, the target values also increase. Next, in order to increase the power of the developed control chart in detecting disturbances in the covariance matrix elements, an improved version is presented with the help of two sensitizing rules 2 out of 3 and 4 out of 5. The performance of the proposed approaches is compared with two control charts ATL and RPLR using a numerical example. The results show that the approach equipped with sensitizing rules performs better than the competing charts in terms of two indices ARL and SDRL.

Highlights

  • Monitor the dispersion of high-dimensional processes using a neural network
  • Study the effect of sensitizing rules on run length properties.
  • Usability when statistical assumptions are violated

Keywords
Dispersion monitoring
High-dimensional process
Neural network-based control chart
Regularization
Sensitizing rule
Random removal layer
Subjects

Copyright © Mir Milad Ghazvini, Ali Salmasnia

 

License

This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.

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System Engineering and Productivity

Articles in Press, Accepted Manuscript
Available Online from 03 September 2025

  • Receive Date 18 July 2025
  • Revise Date 17 August 2025
  • Accept Date 03 September 2025
  • First Publish Date 03 September 2025
  • Publish Date 03 September 2025