A Review of Process Monitoring Method and Rule Extraction Method for Manufacturing Process Quality Control

Haichuan Zhou

doi:10.62051/ijcsit.v2n2.30

Authors

Haichuan Zhou

DOI:

https://doi.org/10.62051/ijcsit.v2n2.30

Keywords:

Process Monitoring; Rule Extraction; Data Characteristics

Abstract

Nowadays, scholars have explored many methods for manufacturing process quality control. This paper presents the current research progress in these two areas in manufacturing process quality control by exploring the two areas of process monitoring and rule extraction, and analyzes the strengths and weaknesses of the current research. In the review of process monitoring, the paper introduces a variety of research methods based on different data characteristics of the manufacturing process, which makes the differentiation of different data characteristics and the applicability of the methods more obvious. In the research review on rule extraction, this paper will introduce the characteristics of excellent rule extraction algorithms and introduce various rule extraction algorithms and their applications. Finally, this paper expects to combine the two to put forward some suggestions on future research directions.

Downloads

Download data is not yet available.

References

Ge Z, Song Z, Gao F. Review of Recent Research on Data-Based Process Monitoring [J]. Industrial & Engineering Chemistry Research, 2013, 52(10): 3543-62.

Venkatasubramanian V, Rengaswamy R, Kavuri S N, et al. A review of process fault detection and diagnosis: Part III: Process history based methods [J]. Computers & Chemical Engineering, 2003, 27(3): 327-46.

Negiz A, Cinar A. Statistical monitoring of multivariable dynamic processes with state-space models [J]. Aiche Journal, 1997, 43(8): 2002-20.

Li Z, Yan X. Fault-Relevant Optimal Ensemble ICA Model for Non-Gaussian Process Monitoring [J]. IEEE Transactions on Control Systems Technology, 2020, 28(6): 2581-90.

Chang P, Kang O, Ding C H, et al. Application of fault monitoring and diagnosis in process industry based on fourth order moment and singular value decomposition [J]. Canadian Journal of Chemical Engineering, 2020, 98(3).

Wang J, Zhao C H. A Gaussian Feature Analytics-Based DISSIM Method for Fine-Grained Non-Gaussian Process Monitoring [J]. Ieee Transactions on Automation Science and Engineering, 2020, 17(4): 2175-81.

Wang Y, Li S, Ling D, et al. A multi-blockNMFmodel fornon-Gaussianprocess monitoring based on the adaptive partition non-negative matrix factorization andBayesian inference [J]. Canadian Journal of Chemical Engineering, 2021, 99(2): 543-57.

Zhang C, Dai X N, Zheng X F, et al. A Novel Monitoring Strategy Combining the Advantages of NPE and GMM [J]. Ieee Access, 2020, 8: 82989-97.

Li Y F, Wang Z J, Zhao T S, et al. Research on a Pattern Recognition Method of Cyclic GMM-FCM Based on Joint Time-Domain Features [J]. Ieee Access, 2021, 9: 1904-17.

Xiao B, Li Y G, Sun B, et al. Decentralized PCA modeling based on relevance and redundancy variable selection and its application to large-scale dynamic process monitoring [J]. Process Safety and Environmental Protection, 2021, 151: 85-100.

Chen Y, Tong C D, Ge Y H, et al. Fault detection based on auto-regressive extreme learning machine for nonlinear dynamic processes [J]. Applied Soft Computing, 2021, 106.

Wan X C, Tong C D, Meng S J, et al. Dynamic process monitoring based on a time-serial multi-block modeling approach [J]. Journal of Process Control, 2020, 89: 22-9.

Cui P, Zhan C J, Yang Y P. Improved nonlinear process monitoring based on ensemble KPCA with local structure analysis [J]. Chemical Engineering Research & Design, 2019, 142: 355-68.

Deng X G, Tian X M, Chen S, et al. Deep Principal Component Analysis Based on Layerwise Feature Extraction and Its Application to Nonlinear Process Monitoring [J]. Ieee Transactions on Control Systems Technology, 2019, 27(6): 2526-40.

Si Y B, Wang Y Q, Zhou D H. Key-Performance-Indicator-Related Process Monitoring Based on Improved Kernel Partial Least Squares [J]. Ieee Transactions on Industrial Electronics, 2021, 68(3): 2626-36.

Chen Q, Liu Z Z, Ma X, et al. Artificial Neural Correlation Analysis for Performance-Indicator-Related Nonlinear Process Monitoring [J]. Ieee Transactions on Industrial Informatics, 2022, 18(2): 1039-49.

Chen Z W, Liang K T, Ding S X, et al. A Comparative Study of Deep Neural Network-Aided Canonical Correlation Analysis-Based Process Monitoring and Fault Detection Methods [J]. Ieee Transactions on Neural Networks and Learning Systems, 2022, 33(11): 6158-72.

Jiang Q C, Yan X F. Learning Deep Correlated Representations for Nonlinear Process Monitoring [J]. Ieee Transactions on Industrial Informatics, 2019, 15(12): 6200-9.

Wang K, Yuan X F, Chen J H, et al. Supervised and semi-supervised probabilistic learning with deep neural networks for concurrent process-quality monitoring [J]. Neural Networks, 2021, 136: 54-62.

Zhang Z H, Jiang T, Zhan C J, et al. Gaussian feature learning based on variational autoencoder for improving nonlinear process monitoring [J]. Journal of Process Control, 2019, 75: 136-55.

Shafi I, Mazhar M F, Fatima A, et al. Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance [J]. Drones, 2023, 7(1).

Tyystj Ärvi T, Virkkunen I, Fridolf P, et al. Automated defect detection in digital radiography of aerospace welds using deep learning [J]. Welding in the World, 2022, 66(4): 643-71.

Zhang J H, Zhou H, IEEE. Particle Swarm Optimization Algorithm based Gaussian Mixture Models for Remote-Sensing Image Recognition; proceedings of the 33rd Chinese Control Conference (CCC), Nanjing, PEOPLES R CHINA, F Jul 28-30, 2014 [C]. 2014.

Odense S, Garcez A D. Extracting M of N Rules from Restricted Boltzmann Machines; proceedings of the 26th International Conference on Artificial Neural Networks (ICANN), Alghero, ITALY, F Sep 11-14, 2017 [C]. 2017.

Ma W P, Zhou X B, Zhu H, et al. A two-stage hybrid ant colony optimization for high-dimensional feature selection [J]. Pattern Recognition, 2021, 116.

Yildirim G, Alatas B. New adaptive intelligent grey wolf optimizer based multi-objective quantitative classification rules mining approaches [J]. Journal of Ambient Intelligence and Humanized Computing, 2021, 12(10): 9611-35.

Rajab K D. New Associative Classification Method Based on Rule Pruning for Classification of Datasets [J]. Ieee Access, 2019, 7: 157783-95.

Hasanpour H, Meibodi R G, Navi K. Improving rule-based classification using Harmony Search [J]. Peerj Computer Science, 2019.

Geng X J, Liang Y, Jiao L M. EARC: Evidential association rule-based classification [J]. Information Sciences, 2021, 547: 202-22.

Mabu S, Higuchi T, Kuremoto T. Semi Supervised Learning for Class Association Rule Mining Using Genetic Network Programming [J]. Ieej Transactions on Electrical and Electronic Engineering, 2020.

Zhou H Y, Hirasawa K. Evolving temporal association rules in recommender system [J]. Neural Computing & Applications, 2019, 31(7): 2605-19.

Ma Z, Zhao Q, Wang S. Fault Diagnosis and Handling of the Two-Dimensional Tracking Servo System for Space [J]. Computational Intelligence and Neuroscience, 2022, 2022: 8174674.

Helal A M, Otero F E B. Data stream classification with ant colony optimisation [J]. International Journal of Intelligent Systems, 2022, 37: 5725 - 51.