Authors:
Safaa J.Alwan,Ruqaia Jwad Kadhim,Hasanain Jalil Neamah Alsaedi,DOI NO:
https://doi.org/10.26782/jmcms.2025.10.00005Keywords:
Artificial Intelligence,Control charts,EWMW,Hybrid Models,Abstract
This paper introduces a next-generation hybrid system for industrial process monitoring and control, integrating advanced statistical process control (SPC) charts with state-of-the-art artificial intelligence (AI) models. By combining robust adaptive charts such as Max-mixed EWMA and Bayesian SPC with deep learning architectures including Transformers, Graph Neural Networks (GNNs), and reinforcement/meta-learning agents, the framework achieves real-time detection, precise diagnosis, and autonomous recovery from process anomalies. Evaluation on a real-world manufacturing dataset demonstrates that the hybrid approach consistently outperforms traditional SPC and standalone neural models across key metrics, including detection delay, false alarm rate, recovery time, and interpretability. The modular architecture allows for flexible extension, human-in-the-loop transparency, and scalable deployment in dynamic, sensor-rich industrial environments. This work sets a new benchmark for smart manufacturing, highlighting the synergistic value of statistical-AI fusion for trustworthy and adaptive quality control.Refference:
I. Ahmad Farooq and Kamran Iqbal , “ A Survey of Reinforcement Learning for Optimization in Automation “ , arXiv :2502.09417v1, 2025.
II. A. Alzarooni , “ Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review “ , arXiv :2501.11310v1, 2025.
III. Andrej Škrlec and Jernej Klemenc , “ Estimating the Strain-Rate-Dependent Parameters of the Johnson-Cook Material Model Using Optimisation Algorithms Combined with a Response Surface” , mathematics , 2020 .
IV. B. Wang et al.”A diagnosis method based on graph neural networks embedded with multirelationships of intrinsic mode functions for multiple mechanical faults”, Defence Technology , 2025
V. Bei Yu et al , “ A Modified Wavenumber Algorithm of Multi-Layered Structures with Oblique Incidence Based on Full-Matrix Capture “ , applied science,2021.
VI. Christian Goetz and Bernhard G. Humm , “ A Hybrid and Modular Integration Concept for Anomaly Detection in Industrial Control Systems “ , AI, 2025.
VII. D. Kim et al , “ Are Self-Attentions Effective for Time Series Forecasting?”, arXiv :2405.16877v3,2024.
VIII. D. Breskuviene and G. Dzemyda , “ Categorical Feature Encoding Techniques for Improved Classifier Performance when Dealing with Imbalanced Data of Fraudulent Transactions “ , INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL , 2023.
IX. Eslam Eldeeb and Hirley Alves , “ Offline and Distributional Reinforcement Learning for Wireless Communications “ , arXiv: 2504.03804v1, 2025 .
X. Francesca Orsola Alfano et al , “ Modelling Deaggregation Due to Normal Carrier–Wall Collision in Dry Powder Inhalers “ , processes , 2022 .
XI. Gejza Dohnal ,”Robust Adaptive Control Charts”, arXiv :1901.03701v1, 2019.
XII. Hadeer Adel et al , “Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm”, mathematics, 2022 .
XIII. Hamdan Thabit et al , “Detecting Malicious .NET Executables Using Extracted Methods Names”, AI , 2025 .
XIV. Isıl Baysal Erez et al , “ Extended MetaLIRS: Meta-learning for Imputation and Regression Selection Model with Explainability for Different Missing Data Mechanisms”, International Journal of Data Science and Analytics , 2025 .
XV. I. Khan ,”Adaptive EWMA control chart using Bayesian approach under ranked set sampling schemes with application to Hard Bake process”, scientific reports, 2023.
XVI. J.Ajadi et al ,”A New Robust Multivariate EWMA Dispersion Control Chart for Individual Observations”, mathematics,2021
XVII. John T. Hancock and Taghi M. Khoshgoftaar , “ Survey on categorical data for neural networks”, Journal of Big Data,2020.
XVIII. J. A. Aldhaibani , M. Q. Mohammed , A. A. Mahmood and M. Sellab Hamza, “Development of wearable textile patch antenna 2.43 GHz for biomedical applications,” Int. J. Adv. Technol. Eng. Explor., vol. 11, no. 111, pp. 2394–7454, 2024. 10.19101/IJATEE.2023.10102312
XIX. J. A. Aldhaibaini, A. Yahya, R. B. Ahmad, A. S. Md Zain, and M. K. Salman, “Performance analysis of two-way multi-user with balance transmitted power of relay in LTE-A cellular networks,” J. Theor. Appl. Inf. Technol., vol. 51, no. 2, pp. 182–190, May 2013.
XX. K. Al-Majdi and Y. S. Mezaal, “New miniature narrow band microstrip diplexer for recent wireless communications,” Electronics, vol. 12, no. 3, p. 716, 2023. 10.3390/electronics12030716.
XXI. Lu Xiao et al. “A graph neural network based bearing fault detection method”, scientific reports,2023.
XXII. M. S. E. Martins et al, “A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems”, applied science,2025.
XXIII. M.Raza,et al ,”Nonparametric mixed exponentially weighted moving average moving average control chart” , Scientific Reports,2024.
XXIV. M. Zhao et al , “ Graph Neural Networks for Virtual Sensing in Complex Systems: Addressing Heterogeneous Temporal Dynamics “ , arXiv:2407.18691v1, 2024 .
XXV. Paul-Eric Dossou et al , “ Development of a Sustainable Industry 4.0 Approach for Increasing the Performance of SMEs “ , processes , 2022 .
XXVI. Ruoyu Sun et al , “ Combining Transformer based Deep Reinforcement Learning with Black-Litterman Model for Portfolio Optimization “ , arXiv:2402.16609 , 2024.
XXVII. S.Malik et al .“Max mixed EWMA control chart for joint monitoring of mean and variance: an application to yogurt packing process”, scientific reports,2024.
XXVIII. S.J.Bu, “ Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption” , energies , 2020.
XXIX. T.N. Fatyanosa et al , “Meta-learning for vessel time series data imputation method Recommendation “, Expert Systems With Applications , 2024.
XXX. W. Xiao et al , “ Spatio-temporal Graph Neural Network for Fault Diagnosis Modeling of Industrial Robot” ,
https://orca.cardiff.ac.uk/id/eprint/178619/2/Contribution120_revised.pdf ,
2025.
XXXI. X. Zhao et al , “ An Intrusion Detection System Based on Genetic Algorithm for Software-Defined Networks” , mathematics , 2022 .
XXXII. Yousef Jaradat el at , “ Review of Data Imputation Techniques in Time Series Data: Comparative Analysis “ , IConTech 2023: International Conference on Technology , 2024 .
XXXIII. Y. S. Mezaal, H. T. Eyyuboglu, and J. K. Ali, “New dual band dual-mode microstrip patch bandpass filter designs based on Sierpinski fractal geometry,” in Proc. 2013 3rd Int. Conf. Adv. Comput. Commun. Technol. (ACCT), Rohtak, India, pp. 348–352, 2013. 10.1109/ACCT.2013.89.
XXXIV. Z.Li et al. “A survey of deep learning for industrial visual anomaly Detection”, Artificial Intelligence Review 2025.