A Novel EMD-1DCNN Framework for Recognizing Concurrent Control Chart Patterns in Autocorrelated Processes

Cang Wu (), Huijuan Hou, Chunli Lei (), Mingliang Wang, Yongjun Du and Wenpo Huang
Additional contact information
Cang Wu: School of Mechanical Engineering, Lanzhou University of Technology, Lanzhou 730050, China
Huijuan Hou: China National Heavy Duty Truck Group Co., Ltd., Jinan 250000, China
Chunli Lei: School of Mechanical Engineering, Lanzhou University of Technology, Lanzhou 730050, China
Mingliang Wang: Science and Technology on Vacuum Technology and Physics Laboratory, Lanzhou Institute of Physics, Lanzhou 730000, China
Yongjun Du: School of Economics and Management, Lanzhou University of Technology, Lanzhou 730050, China
Wenpo Huang: School of Management, Hangzhou Dianzi University, Hangzhou 310018, China

Mathematics, 2025, vol. 13, issue 22, 1-19

Abstract: Control chart pattern recognition was initially focused on single patterns with the assumption of normal, independent, and identical distribution. In practice, though, these assumptions are rarely valid in manufacturing processes, due to numerous influencing factors and short intervals in data collecting. It is necessary to consider that the inherent disturbance is autocorrelated and that two single patterns appear at the same time. This study presents a novel framework integrating Empirical Mode Decomposition (EMD) and one-dimensional Convolutional Neural Networks (1DCNN) with feature component selection for recognizing concurrent control chart patterns in autocorrelated manufacturing processes. We assume the inherent disturbance follows a first-order autoregressive (AR (1)) process and simulate eleven concurrent patterns. Then, the EMD method decomposes the concurrent pattern into a series of feature components, wherein the correlation coefficient is employed as the index by which to select the two feature components. Finally, the selected feature components and raw data are combined to create a feature vector that acts as the input for the 1DCNN model. The simulation results demonstrate that the proposed model achieves a recognition accuracy of 92.39%, outperforming both the singular spectrum analysis–support vector machine (SSA-SVM) and the singular spectrum analysis–random forest (SSA-RF) methods in terms of accuracy and robustness.

Keywords: autocorrelated process; control chart pattern recognition; empirical mode decomposition; feature components selection; one-dimensional convolutional neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/22/3577/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/22/3577/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:22:p:3577-:d:1789938

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().