| Issue |
MATEC Web Conf.
Volume 413, 2025
International Conference on Measurement, AI, Quality and Sustainability (MAIQS 2025)
|
|
|---|---|---|
| Article Number | 07004 | |
| Number of page(s) | 7 | |
| Section | Advances in Quality Management | |
| DOI | https://doi.org/10.1051/matecconf/202541307004 | |
| Published online | 01 October 2025 | |
Design and application of a robust multivariate control chart for gas PE pipe production
1 College of Quality and Standardization, China Jiliang University, Hangzhou 310018, China
2 Linhai Weixing New-type Building Materials Co., Ltd., Linhai 317000, China
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
In the production of gas polyethylene (PE) pipelines, quality characteristics such as ovality, outer diameter, and wall thickness often exhibit unknown distributions and complex inter-variable correlations. Traditional parametric control charts are prone to false alarms and missed detections under such conditions. This study proposes a Multivariate Lepage-type Projection Nonparametric (MLPN) control chart, integrating Vander Waerden and Klotz tests with a robust covariance estimation framework. The method first transforms multivariate observations into a single test statistic to jointly monitor shifts in location and scale parameters via nonparametric rank-based statistics. It then introduces a projection-based weighting scheme to mitigate the influence of outliers on covariance estimation, thereby enhancing the robustness and applicability of the control chart under complex conditions. Monte Carlo simulations and real-world PE pipe production data demonstrate that the proposed chart achieves accurate detection of process shifts, with reduced false alarm rates and improved sensitivity, providing an efficient and reliable tool for quality monitoring in continuous manufacturing processes.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

