{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T06:14:33Z","timestamp":1770876873379,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["23KJD120002"],"award-info":[{"award-number":["23KJD120002"]}]},{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["24KJB520005"],"award-info":[{"award-number":["24KJB520005"]}]},{"name":"Jiangsu Province Education Science Planning Project","award":["B-b\/2024\/01\/166"],"award-info":[{"award-number":["B-b\/2024\/01\/166"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Integrating machine learning (ML) with Statistical Process Control (SPC) is important for Industry 4.0 environments. Contemporary manufacturing data exhibit high-dimensionality, autocorrelation, non-stationarity, and class imbalance, which challenge classical SPC assumptions. This systematic review, conducted following the PRISMA 2020 guidelines, provides a problem-driven synthesis that links these data challenges to corresponding methodological families in ML-based SPC. Specifically, we review approaches for (1) high-dimensional and redundant data (dimensionality reduction and feature selection), (2) autocorrelated and dynamic processes (time-series and state-space models), and (3) data scarcity and imbalance (cost-sensitive learning, generative modeling, and transfer learning). Nonlinearity is treated as a cross-cutting property within each category. For each, we outline the mathematical rationale of representative algorithms and illustrate their use with industrial examples. We also summarize open issues in interpretability, thresholding, and real-time deployment. This review offers structured guidance for selecting ML techniques suited to complex manufacturing data and for designing reliable online monitoring pipelines.<\/jats:p>","DOI":"10.3390\/e28020151","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T14:52:36Z","timestamp":1769698356000},"page":"151","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mathematical and Algorithmic Advances in Machine Learning for Statistical Process Control: A Systematic Review"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0977-6679","authenticated-orcid":false,"given":"Yulong","family":"Qiao","sequence":"first","affiliation":[{"name":"School of Information Technology, Jiangsu Open University, Nanjing 210036, China"}]},{"given":"Tingting","family":"Han","sequence":"additional","affiliation":[{"name":"School of Automotive Studies, Tongji University, Shanghai 201804, China"},{"name":"State Key Laboratory of Space Power-Sources Technology, Shanghai Institute of Space Power-Sources, 2965 Dongchuan Road, Shanghai 200245, China"}]},{"given":"Zixing","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Technology, Jiangsu Open University, Nanjing 210036, China"}]},{"given":"Ge","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Information Technology, Jiangsu Open University, Nanjing 210036, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1749-8653","authenticated-orcid":false,"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Technology, Jiangsu Open University, Nanjing 210036, China"}]},{"given":"Qin","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Network Security, Jinling Institute of Technology, Nanjing 211169, China"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"ref_1","unstructured":"Montgomery, D.C. (2009). Introduction to Statistical Quality Control, John Wiley & Sons. [7th ed.]."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1080\/00224065.2014.11917955","article-title":"Some current directions in the theory and application of statistical process monitoring","volume":"46","author":"Woodall","year":"2014","journal-title":"J. Qual. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12861","DOI":"10.1109\/JIOT.2021.3139827","article-title":"The duo of artificial intelligence and big data for industry 4.0: Applications, techniques, challenges, and future research directions","volume":"9","author":"Jagatheesaperumal","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Xu, J., Kovatsch, M., Mattern, D., Mazza, F., Harasic, M., Paschke, A., and Lucia, S. (2022). A review on AI for smart manufacturing: Deep learning challenges and solutions. Appl. Sci., 12.","DOI":"10.3390\/app12168239"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107282","DOI":"10.1016\/j.engappai.2023.107282","article-title":"The partitioning ensemble control chart for on-line monitoring of high-dimensional image-based quality characteristics","volume":"127","author":"Yeganeh","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1080\/00401706.2018.1562988","article-title":"A new process control chart for monitoring short-range serially correlated data","volume":"62","author":"Qiu","year":"2020","journal-title":"Technometrics"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110725","DOI":"10.1016\/j.cie.2024.110725","article-title":"Monitoring bivariate autocorrelated process using a deep learning-based control chart: A case study on the car manufacturing industry","volume":"199","author":"Yeganeh","year":"2025","journal-title":"Comput. Ind. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chu, H., Dong, Y., Cheng, Q., Yan, J., Zhao, Y., Cao, J., Zhang, C., and Chen, X. (2024). Pattern recognition of control charts based on data feature enhancement and ensemble learning of classifiers for dimensional accuracy of products. Int. J. Prod. Res., 1\u201320.","DOI":"10.1080\/00207543.2024.2387095"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.isatra.2024.09.001","article-title":"Concurrent control chart pattern recognition in manufacturing processes based on zero-shot learning","volume":"154","author":"Li","year":"2024","journal-title":"ISA Trans."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"113839","DOI":"10.1016\/j.knosys.2025.113839","article-title":"Kernel-based composite control chart for nonlinear conditionally heteroscedastic time series","volume":"325","author":"Kim","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"109273","DOI":"10.1016\/j.ress.2023.109273","article-title":"Mission Reliability-Driven Risk-Based Predictive Maintenance Approach of Multistate Manufacturing System","volume":"236","author":"Liao","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"109693","DOI":"10.1016\/j.ress.2023.109693","article-title":"Mission Reliability-Centered Opportunistic Maintenance Approach for Multistate Manufacturing Systems","volume":"241","author":"Yang","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1080\/00401706.2000.10485713","article-title":"A new SPC monitoring method: The ARMA chart","volume":"42","author":"Jiang","year":"2000","journal-title":"Technometrics"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"110894","DOI":"10.1016\/j.cie.2025.110894","article-title":"Memory control chart based on machine learning technique for efficient process monitoring","volume":"201","author":"Zaman","year":"2025","journal-title":"Comput. Ind. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Visconti, P., Rausa, G., Del-Valle-Soto, C., Vel\u00e1zquez, R., Cafagna, D., and De Fazio, R. (2024). Machine learning and IoT-based solutions in industrial applications for Smart Manufacturing: A critical review. Future Internet, 16.","DOI":"10.3390\/fi16110394"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1080\/00401706.2024.2327341","article-title":"Statistical Process Monitoring from Industry 2.0 to Industry 4.0: Insights into Research and Practice","volume":"66","author":"Colosimo","year":"2024","journal-title":"Technometrics"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1080\/16843703.2024.2395745","article-title":"Comprehensive Review of High-Dimensional Monitoring Methods: Trends, Insights, and Interconnections","volume":"22","author":"Ahmed","year":"2025","journal-title":"Qual. Technol. Quant. Manag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TSTE.2018.2801625","article-title":"Wind turbine fault detection and identification through PCA-based optimal variable selection","volume":"9","author":"Wang","year":"2018","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bamdad, S. (2025). Leveraging machine learning and decision analytics for sustainable and resilient environmental monitoring in metal processing industries: A step towards Industry 5.0. Int. J. Prod. Res., 1\u201327.","DOI":"10.1080\/00207543.2025.2487567"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1007\/s11740-025-01363-w","article-title":"Advanced Real-Time Monitoring Techniques for High-Dimensional Data Streams in Industrial Two-Sample Analysis","volume":"19","author":"Mahmood","year":"2025","journal-title":"Prod. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103136","DOI":"10.1109\/ACCESS.2022.3210189","article-title":"Traffic anomaly detection in wireless sensor networks based on principal component analysis and deep convolution neural network","volume":"10","author":"Yao","year":"2022","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.chemolab.2019.02.001","article-title":"Optimal design of the synthetic control chart for monitoring the multivariate coefficient of variation","volume":"186","author":"Khaw","year":"2019","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106028","DOI":"10.1016\/j.cie.2019.106028","article-title":"Dual multivariate CUSUM mean charts","volume":"137","author":"Haq","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Attouri, K., Mansouri, M., Hajji, M., and Bouzrara, K. (2025, January 3\u20135). Efficient Fault Detection in Nonlinear Industrial Process: A Reduced Kernel PCA-based Spectral Clustering Approach. Proceedings of the 2025 International Conference for Artificial Intelligence, Applications, Innovation and Ethics (AI2E), Muscat, Oman.","DOI":"10.1109\/AI2E64943.2025.10983361"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"M\u00fcller, N.M., Roschmann, S., Khan, S., Sperl, P., and B\u00f6ttinger, K. (2024). Shortcut detection with variational autoencoders. Proceedings of the 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 30 June\u20135 July 2024, IEEE.","DOI":"10.1109\/IJCNN60899.2024.10650671"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Marconato, E., Passerini, A., and Teso, S. (2023). Interpretability is in the mind of the beholder: A causal framework for human-interpretable representation learning. Entropy, 25.","DOI":"10.3390\/e25121574"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.jmsy.2024.12.003","article-title":"Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring","volume":"78","author":"McKinney","year":"2025","journal-title":"J. Manuf. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3334","DOI":"10.1002\/qre.70041","article-title":"Hybrid Statistical Process Monitoring of Wire Arc Additive Manufacturing with Frequency-Informed Deep Learning","volume":"41","author":"Mattera","year":"2025","journal-title":"Qual. Reliab. Eng. Int."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"120460","DOI":"10.1016\/j.ces.2024.120460","article-title":"Robust statistical industrial fault monitoring: A machine learning-based distributed CCA and low frequency control charts","volume":"299","author":"Ali","year":"2024","journal-title":"Chem. Eng. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111111","DOI":"10.1016\/j.cie.2025.111111","article-title":"Nonparametric monitoring of high-dimensional processes via EWMA control charts based on random forest learning","volume":"204","author":"Wu","year":"2025","journal-title":"Comput. Ind. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"111227","DOI":"10.1016\/j.engappai.2025.111227","article-title":"Enhanced process monitoring using machine learning-based control charts for poisson-distributed data","volume":"157","author":"Mukhtiar","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1080\/16843703.2024.2442783","article-title":"A multivariate finite horizon production control chart for monitoring the food production process","volume":"22","author":"Zhou","year":"2025","journal-title":"Qual. Technol. Quant. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"119660","DOI":"10.1016\/j.eswa.2023.119660","article-title":"A network surveillance approach using machine learning based control charts","volume":"219","author":"Yeganeh","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1080\/08982112.2023.2220773","article-title":"A new multivariate control chart based on the isolation forest algorithm","volume":"36","author":"Wang","year":"2024","journal-title":"Qual. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, Z., Shi, J., and Van Leeuwen, M. (2024, January 14\u201320). Graph neural networks based log anomaly detection and explanation. Proceedings of the 2024 IEEE\/ACM 46th International Conference on Software Engineering: Companion Proceedings, Lisbon, Portugal.","DOI":"10.1145\/3639478.3643084"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1714","DOI":"10.1109\/TII.2024.3485805","article-title":"Dynamic Graph Embedding PCA to Extract Spatio\u2013Temporal Information for Fault Detection","volume":"21","author":"Bao","year":"2025","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1214\/25-EJS2344","article-title":"Mixed moving average field guided learning for spatio-temporal data","volume":"19","author":"Curato","year":"2025","journal-title":"Electron. J. Stat."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2373","DOI":"10.1002\/qre.3809","article-title":"Machine Learning Control Charts for Monitoring Spatio-Temporal Data Streams","volume":"41","author":"Zhou","year":"2025","journal-title":"Qual. Reliab. Eng. Int."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2687","DOI":"10.1002\/qre.2551","article-title":"Deep recurrent neural network-based residual control chart for autocorrelated processes","volume":"35","author":"Chen","year":"2019","journal-title":"Qual. Reliab. Eng. Int."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"119140","DOI":"10.1016\/j.eswa.2022.119140","article-title":"What is the best RNN-cell structure to forecast each time series behavior?","volume":"215","author":"Khaldi","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"350","DOI":"10.3390\/make4020015","article-title":"An attention-based ConvLSTM autoencoder with dynamic thresholding for unsupervised anomaly detection in multivariate time series","volume":"4","author":"Tayeh","year":"2022","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wu, Z., Rincon, D., and Christofides, P.D. (2019). Real-time optimization and control of nonlinear processes using machine learning. Mathematics, 7.","DOI":"10.3390\/math7100890"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"122310","DOI":"10.1016\/j.apenergy.2023.122310","article-title":"Surrogate empowered Sim2Real transfer of deep reinforcement learning for ORC superheat control","volume":"356","author":"Lin","year":"2024","journal-title":"Appl. Energy"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.1002\/qre.3760","article-title":"Adaptive CUSUM control chart utilizing supervised learning for monitoring the process location parameter: A case study application","volume":"41","author":"Zaman","year":"2025","journal-title":"Qual. Reliab. Eng. Int."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tauqeer, F., Riaz, M., Zaman, B., and Arshad, I.A. (2025). A Simulation-Based Bayesian Multivariate Adaptive EWMA Framework with Hybrid Score Functions for Monitoring Water Quality. J. Stat. Comput. Simul., 1\u201345.","DOI":"10.1080\/00949655.2025.2584733"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2567","DOI":"10.1002\/qre.3797","article-title":"A Machine Learning Approach to Adaptive EWMA Control Charts: Insights from Cardiac Surgery Data","volume":"41","author":"Abbas","year":"2025","journal-title":"Qual. Reliab. Eng. Int."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1080\/00949655.2023.2229472","article-title":"Dual-Rank Ranked Set Sampling","volume":"94","author":"Taconeli","year":"2024","journal-title":"J. Stat. Comput. Simul."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5913","DOI":"10.1007\/s10586-024-04270-4","article-title":"Designing a Modified Feature Aggregation Model with Hybrid Sampling Techniques for Network Intrusion Detection","volume":"27","author":"Biyyapu","year":"2024","journal-title":"Clust. Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2008","DOI":"10.1109\/LRA.2024.3352358","article-title":"PKU-GoodsAD: A supermarket goods dataset for unsupervised anomaly detection and segmentation","volume":"9","author":"Zhang","year":"2024","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Li, Y., and Vasconcelos, N. (2019, January 15\u201320). Repair: Removing representation bias by dataset resampling. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00980"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2398","DOI":"10.1080\/00207543.2023.2217299","article-title":"Digital Twin simulation models: A validation method based on machine learning and control charts","volume":"62","author":"Campos","year":"2024","journal-title":"Int. J. Prod. Res."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Mih, A.N., Cao, H., Pickard, J., Wachowicz, M., and Dubay, R. (2023). TransferD2: Automated Defect Detection Approach in Smart Manufacturing using Transfer Learning Techniques. Proceedings of the 2023 IEEE International Conference on Omni-Layer Intelligent Systems (COINS), Berlin, Germany, 23\u201325 July 2023, IEEE.","DOI":"10.1109\/COINS57856.2023.10189312"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Aburakhia, S., Tayeh, T., Myers, R., and Shami, A. (2022). Similarity-based predictive maintenance framework for rotating machinery. Proceedings of the 2022 5th International Conference on Communications, Signal Processing, and Their Applications (ICCSPA), Cairo, Egypt, 27\u201329 December 2022, IEEE.","DOI":"10.1109\/ICCSPA55860.2022.10019121"},{"key":"ref_55","unstructured":"Ratner, A., Hancock, B., Dunnmon, J., Sala, F., Pandey, S., and R\u00e9, C. (February, January 27). Training complex models with multi-task weak supervision. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"110262","DOI":"10.1016\/j.cie.2024.110262","article-title":"Self-Starting Monitoring Schemes for Small-Sample Poisson Profiles Based on Transfer Learning","volume":"192","author":"Shang","year":"2024","journal-title":"Comput. Ind. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"101918","DOI":"10.1016\/j.aei.2023.101918","article-title":"A Gray Correlation Based Bayesian Network Model for Fault Source Diagnosis of Multistage Process\u2014Small Sample Manufacturing System","volume":"56","author":"Chu","year":"2023","journal-title":"Adv. Eng. Inform."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.1002\/qre.2538","article-title":"Hybrid approach for remaining useful life prediction of ball bearings","volume":"35","author":"Wang","year":"2019","journal-title":"Qual. Reliab. Eng. Int."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Kruschel, S., Hambauer, N., Weinzierl, S., Zilker, S., Kraus, M., and Zschech, P. (2025). Challenging the performance-interpretability trade-off: An evaluation of interpretable machine learning models. Bus. Inf. Syst. Eng., 1\u201325.","DOI":"10.1007\/s12599-024-00922-2"},{"key":"ref_60","unstructured":"Alpaydin, E. (2020). Introduction to Machine Learning, MIT Press. [4th ed.]."},{"key":"ref_61","first-page":"9866","article-title":"On the safety of interpretable machine learning: A maximum deviation approach","volume":"35","author":"Wei","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Qian, X., Zhang, C., Yella, J., Huang, Y., Huang, M.C., and Bom, S. (2021). Soft sensing model visualization: Fine-tuning neural network from what model learned. Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 15\u201318 December 2021, IEEE.","DOI":"10.1109\/BigData52589.2021.9671850"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s40860-024-00231-1","article-title":"Surveying neuro-symbolic approaches for reliable artificial intelligence of things","volume":"10","author":"Lu","year":"2024","journal-title":"J. Reliab. Intell. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Shahzad, F., Huang, Z., and Memon, W.H. (2022). Process Monitoring Using Kernel PCA and Kernel Density Estimation-Based SSGLR Method for Nonlinear Fault Detection. Appl. Sci., 12.","DOI":"10.3390\/app12062981"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Marsh, I., Paladi, N., Abrahamsson, H., Gustafsson, J., Sj\u00f6berg, J., Johnsson, A., Sk\u00f6ldstr\u00f6m, P., Dowling, J., Monti, P., and Vruna, M. (2021, January 11\u201313). Evolving 5G: ANIARA, an edge-cloud perspective. Proceedings of the 18th ACM International Conference on Computing Frontiers, Virtual.","DOI":"10.1145\/3457388.3458622"},{"key":"ref_66","first-page":"1782","article-title":"SamurAI: A versatile IoT node with event-driven wake-up and embedded ML acceleration","volume":"58","author":"Tain","year":"2022","journal-title":"IEEE J. Solid-State Circuits"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Jena, S., Pulkit, A., Singh, K., Banerjee, A., Joshi, S., Ganesh, A., Singh, D., and Bhavsar, A. (2024). Unified anomaly detection methods on edge device using knowledge distillation and quantization. Proceedings of the International Workshop on Reproducible Research in Pattern Recognition, Kolkata, India, 1 December 2024, Springer.","DOI":"10.1007\/978-3-031-97822-7_5"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/MCOM.001.2000200","article-title":"Opportunities of federated learning in connected, cooperative, and automated industrial systems","volume":"59","author":"Savazzi","year":"2021","journal-title":"IEEE Commun. Mag."},{"key":"ref_69","unstructured":"Hsieh, K., Phanishayee, A., Mutlu, O., and Gibbons, P. (2020, January 13\u201318). The non-iid data quagmire of decentralized machine learning. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/TPAMI.2022.3140249","article-title":"A general descent aggregation framework for gradient-based bi-level optimization","volume":"45","author":"Liu","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1109\/TBDATA.2022.3230335","article-title":"Asynchronous parallel incremental block-coordinate descent for decentralized machine learning","volume":"9","author":"Chen","year":"2022","journal-title":"IEEE Trans. Big Data"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/3502299","article-title":"Scaling beyond bandwidth limitations: Wireless control with stability guarantees under overload","volume":"6","author":"Mager","year":"2022","journal-title":"ACM Trans. Cyber-Phys. Syst."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.jmsy.2024.04.023","article-title":"Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization","volume":"75","author":"Karkaria","year":"2024","journal-title":"J. Manuf. Syst."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Haindl, P., Buchgeher, G., Khan, M., and Moser, B. (2022, January 21\u201329). Towards a reference software architecture for human-ai teaming in smart manufacturing. Proceedings of the ACM\/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results, Pittsburgh, PA, USA.","DOI":"10.1145\/3510455.3512788"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.jmsy.2022.05.005","article-title":"Toward human-centric smart manufacturing: A human-cyber-physical systems (HCPS) perspective","volume":"63","author":"Wang","year":"2022","journal-title":"J. Manuf. 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