{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T17:34:07Z","timestamp":1786210447869,"version":"3.56.0"},"reference-count":103,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T00:00:00Z","timestamp":1577059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI\/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud\/fog\/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human\u2013machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.<\/jats:p>","DOI":"10.3390\/s20010109","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T05:56:15Z","timestamp":1577166975000},"page":"109","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":306,"title":["Tackling Faults in the Industry 4.0 Era\u2014A Survey of Machine-Learning Solutions and Key Aspects"],"prefix":"10.3390","volume":"20","author":[{"given":"Angelos","family":"Angelopoulos","sequence":"first","affiliation":[{"name":"General Department, National and Kapodistrian University of Athens, Thesi skliro, Psahna, 34400 Evia, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1077-0047","authenticated-orcid":false,"given":"Emmanouel T.","family":"Michailidis","sequence":"additional","affiliation":[{"name":"Telecommunications, Signal Processing and Intelligent Systems (TelSiP) Research Laboratory, Department of Electrical and Electronics Engineering, School of Engineering, University of West Attica, Ancient Olive Grove Campus, 12244 Aigaleo, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9210-9796","authenticated-orcid":false,"given":"Nikolaos","family":"Nomikos","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Systems Engineering, School of Engineering, University of the Aegean, 83200 Samos, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5146-5954","authenticated-orcid":false,"given":"Panagiotis","family":"Trakadas","sequence":"additional","affiliation":[{"name":"General Department, National and Kapodistrian University of Athens, Thesi skliro, Psahna, 34400 Evia, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8943-6641","authenticated-orcid":false,"given":"Antonis","family":"Hatziefremidis","sequence":"additional","affiliation":[{"name":"General Department, National and Kapodistrian University of Athens, Thesi skliro, Psahna, 34400 Evia, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stamatis","family":"Voliotis","sequence":"additional","affiliation":[{"name":"General Department, National and Kapodistrian University of Athens, Thesi skliro, Psahna, 34400 Evia, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Theodore","family":"Zahariadis","sequence":"additional","affiliation":[{"name":"General Department, National and Kapodistrian University of Athens, Thesi skliro, Psahna, 34400 Evia, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6505","DOI":"10.1109\/ACCESS.2017.2783682","article-title":"Smart Factory of Industry 4.0: Key Technologies, Application, and Challenges","volume":"6","author":"Chen","year":"2017","journal-title":"IEEE Access"},{"key":"ref_2","first-page":"1","article-title":"Industry 4.0: A Survey on Technologies, Applications and Open Research Issues","volume":"6","author":"Lu","year":"2017","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.engappai.2018.11.007","article-title":"Industry 4.0: A Bibliometric Analysis and Detailed Overview","volume":"78","author":"Muhuri","year":"2019","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2941","DOI":"10.1080\/00207543.2018.1444806","article-title":"Industry 4.0: State of the Art and Future Trends","volume":"56","author":"Xu","year":"2018","journal-title":"Int. J. Prod. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1631\/FITEE.1601885","article-title":"Applications of Artificial Intelligence in Intelligent Manufacturing: A Review","volume":"18","author":"Li","year":"2017","journal-title":"Front. Inf. Tech. Electron. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.procir.2016.08.005","article-title":"A Categorical Framework of Manufacturing for Industry 4.0 and Beyond","volume":"52","author":"Qin","year":"2016","journal-title":"Procedia CIRP"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/J.ENG.2017.05.015","article-title":"Intelligent Manufacturing in the Context of Industry 4.0: A Review","volume":"3","author":"Zhong","year":"2017","journal-title":"Engineering"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Oztemel, E., and Gursev, S. (2018). Literature Review of Industry 4.0 and Related Technologies. J. Intell. Manuf.","DOI":"10.1007\/s10845-018-1433-8"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1016\/j.cirp.2016.06.005","article-title":"Cyber-physical systems in manufacturing","volume":"65","author":"Monostory","year":"2016","journal-title":"CIRP Ann. Manuf. Tech."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"78238","DOI":"10.1109\/ACCESS.2018.2884906","article-title":"A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective","volume":"6","author":"Xu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1109\/JPROC.2018.2878265","article-title":"The Tactile Internet for Industries: A Review","volume":"107","author":"Aijaz","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.aei.2016.11.007","article-title":"A Review of Essential Standards and Patent Landscapes for Internet of Things: A Key Enabler for Industry 4.0","volume":"33","author":"Trappey","year":"2017","journal-title":"Adv. Eng. Inf."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ruppert, T., Jasko, S., Holczinger, T., and Abinyi, J. (2018). Enabling Technologies for Operator 4.0: A Survey. Appl. Sci., 8.","DOI":"10.3390\/app8091650"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1016\/j.procir.2016.01.129","article-title":"Opportunities of Sustainable Manufacturing in Industry 4.0","volume":"40","author":"Stock","year":"2016","journal-title":"Procedia CIRP"},{"key":"ref_15","first-page":"23","article-title":"Machine Learning in Manufacturing: Advantages, Challenges, and Applications","volume":"4","author":"Wuest","year":"2016","journal-title":"Prod. Manuf. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.future.2019.04.020","article-title":"The Role of Big Data Analytics in Industrial Internet of Things","volume":"99","author":"Rehman","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3585","DOI":"10.1109\/ACCESS.2018.2793265","article-title":"Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison","volume":"6","author":"Qi","year":"2018","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.jmsy.2018.01.006","article-title":"Data-Driven Smart Manufacturing","volume":"48","author":"Tao","year":"2018","journal-title":"J. Manuf. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"20590","DOI":"10.1109\/ACCESS.2017.2756872","article-title":"Data Mining and Analytics in the Process Industry: The Role of Machine Learning","volume":"5","author":"Ge","year":"2017","journal-title":"IEEE Access"},{"key":"ref_20","first-page":"555","article-title":"Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry","volume":"5","author":"Kim","year":"2018","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1080\/17517575.2018.1442934","article-title":"Big Data for Cyber Physical Systems in Industry 4.0: A Survey","volume":"13","author":"Xu","year":"2019","journal-title":"Int. J. Enterp. Inf. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sonntag, D., Zillner, S., Smagt, P., and Lorincz, A. (2017). Overview of CPS for Smart Factories Project: Deep learning, Knowledge Acquisition, Anomaly Detection and Intelligent User Interfaces. Industrial Internet of Things, Springer.","DOI":"10.1007\/978-3-319-42559-7_19"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.jmsy.2018.01.003","article-title":"Deep Learning for Smart Manufacturing: Methods and Applications","volume":"48","author":"Wang","year":"2018","journal-title":"J. Manuf. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Reis, M.S., and Gins, G. (2017). Industrial Process Monitoring in the Big Data\/Industry 4.0 Era: From Detection, to Diagnosis, to Prognosis. Processes, 5.","DOI":"10.3390\/pr5030035"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ramotsoela, D., Abu-Mahfouz, A., and Hancke, G. (2018). A Survey of Anomaly Detection in Industrial Wireless Sensor Networks with Critical Water System Infrastructure as a Case Study. Sensors, 18.","DOI":"10.3390\/s18082491"},{"key":"ref_26","first-page":"545","article-title":"Artificial Intelligence\/Machine Learning in Manufacturing and Inspection: A GE Perspective","volume":"44","author":"Aggour","year":"2019","journal-title":"Mach. Learn. Revol. Mater. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1007\/s00170-019-03988-5","article-title":"A Review of Machine Learning for the Optimization of Production Process","volume":"104","author":"Weichert","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"17543","DOI":"10.1109\/ACCESS.2017.2741105","article-title":"Industrial Big Data Analysis in Smart Factory: Current Status and Reasearch Strategies","volume":"5","author":"Xu","year":"2017","journal-title":"IEEE Access"},{"key":"ref_29","first-page":"1","article-title":"Data and Knowledge Mining with Big Data towards Smart Production","volume":"9","author":"Cheng","year":"2018","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4674","DOI":"10.1109\/TII.2018.2855198","article-title":"Deploying Fog Computing in Industrial Internet of Things and Industry 4.0","volume":"14","author":"Aazam","year":"2018","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Trakadas, P., Nomikos, N., Michailidis, E.T., Zahariadis, T., Facca, F.M., Breitgand, D., Rizou, S., Masip, X., and Gkonis, P. (2019). Hybrid Clouds for Data-Intensive, 5G-Enabled IoT Applications: An Overview, Key Issues and Relevant Architecture. Sensors, 19.","DOI":"10.3390\/s19163591"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.mfglet.2018.01.005","article-title":"A Fog Computing Industrial Cyber-Physical System for Embedded Low-Latency Machine Learning Industry 4.0 Applications","volume":"15","author":"Gallagher","year":"2018","journal-title":"Manuf. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"55419","DOI":"10.1109\/ACCESS.2018.2871724","article-title":"Artificial Intelligence for Cloud-Assisted Smart Factory","volume":"6","author":"Wan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.jmsy.2017.02.007","article-title":"Framework and Development of Fault Detection Classification Using IoT Device and Cloud Environment","volume":"43","author":"Lee","year":"2017","journal-title":"J. Manuf. Syst."},{"key":"ref_35","unstructured":"(2019, December 22). Intel\u00a9 Internet of Things (IoT) Developer Kit: IoT Cloud-Based Analytics UserGuide. Available online: https:\/\/software.intel.com\/en-us\/iot\/home."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4665","DOI":"10.1109\/TII.2018.2842821","article-title":"Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing","volume":"14","author":"Li","year":"2018","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lavassani, M., Forsstr\u00f6m, S., Jennehag, U., and Zhang, T. (2018). Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT. Sensors, 18.","DOI":"10.3390\/s18051532"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4235","DOI":"10.1109\/TII.2019.2902878","article-title":"Artificial Intelligence-Driven Mechanism for Edge Computing-Based Industrial Applications","volume":"15","author":"Sodhro","year":"2019","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.jmsy.2017.02.011","article-title":"A Fog Computing-Based Framework for Process Monitoring and Prognosis in Cyber-Manufacturing","volume":"43","author":"Wu","year":"2017","journal-title":"J. Manuf. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Maier, A., Schriegel, S., and Niggemann, O. (2017). Big Data and Machine Learning for the Smart Factory\u2014Solutions for Condition Monitoring, Diagnosis and Optimization. Industrial Internet of Things, Springer.","DOI":"10.1007\/978-3-319-42559-7_18"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1109\/TCAD.2012.2234827","article-title":"Board-Level Functional Fault Diagnosis Using Artificial Neural Networks, Support-Vector Machines, and Weighted-Majority Voting","volume":"32","author":"Ye","year":"2013","journal-title":"IEEE Trans. Comput. Aided Des. Integr. Circuits Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.ymssp.2010.07.013","article-title":"Natural Computing for Mechanical Systems Research: A Tutorial Overview","volume":"20","author":"Worden","year":"2011","journal-title":"Mech. Syst. Sig. Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1109\/TCAD.2015.2481859","article-title":"Efficient Board-Level Functional Fault Diagnosis With Missing Syndromes","volume":"35","author":"Jin","year":"2016","journal-title":"IEEE Trans. Comput. Aided Des. Integr. Circuits Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The Weka Data Mining Software: An Update","volume":"11","author":"Hall","year":"2009","journal-title":"ACM SIGKDD Explor. Newslett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4065","DOI":"10.1109\/TNNLS.2017.2751612","article-title":"Classification of Imbalanced Data by Oversampling in Kernel Space of Support Vector Machines","volume":"29","author":"Mathew","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"56198","DOI":"10.1109\/ACCESS.2019.2912631","article-title":"Concept Drift Detection and Adaption in Big Imbalance Industrial IoT Data Using an Ensemble Learning Method of Offline Classifiers","volume":"7","author":"Lin","year":"2019","journal-title":"IEEE Access"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1109\/TSM.2016.2602226","article-title":"Performance of Machine Learning Algorithms for Class-Imbalanced Process Fault Detection Problems","volume":"29","author":"Lee","year":"2016","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Syafrudin, M., Alfian, G., Fitriyani, N.L., and Rhee, J. (2018). Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing. Sensors, 18.","DOI":"10.3390\/s18092946"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3137","DOI":"10.1109\/TIE.2016.2519325","article-title":"An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data","volume":"63","author":"Lei","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Yang, Z.-X., Wang, X.-B., and Zhong, J.-H. (2016). Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach. Energies, 9.","DOI":"10.3390\/en9060379"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.neucom.2010.02.019","article-title":"Optimization Method Based Extreme Learning Machine for Classification","volume":"74","author":"Huang","year":"2010","journal-title":"Neurocomputing"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1016\/j.promfg.2017.07.091","article-title":"Machine Learning-Based CPS for Clustering High Throughput Machining Cycle Conditions","volume":"10","author":"Bielza","year":"2017","journal-title":"Procedia Manuf."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4973","DOI":"10.1109\/TIE.2017.2767540","article-title":"LiftingNet: A Novel Deep Learning Network With Layerwise Feature Learning From Noisy Mechanical Data for Fault Classification","volume":"65","author":"Pan","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Sohaib, M., Kim, C.H., and Kim, J.M. (2017). A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis. Sensors, 17.","DOI":"10.3390\/s17122876"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/TIE.2018.2807414","article-title":"Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification","volume":"66","author":"Luo","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"56880","DOI":"10.1109\/ACCESS.2019.2914181","article-title":"Spur Gear Fault Diagnosis Using a Multilayer Gated Recurrent Unit Approach With Vibration Signal","volume":"7","author":"Tao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"32037","DOI":"10.1109\/ACCESS.2019.2903295","article-title":"A New Snapshot Ensemble Convolutional Neural Network for Fault Diagnosis","volume":"7","author":"Wen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3077","DOI":"10.1109\/TII.2019.2902274","article-title":"Fault Detection and Isolation in Industrial Processes Using Deep Learning Approaches","volume":"15","author":"Iqbal","year":"2019","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Moyne, J., and Iskandar, J. (2017). Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing. Processes, 5.","DOI":"10.3390\/pr5030039"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.inffus.2018.10.005","article-title":"Data Fusion and Machine Learning for Industrial Prognosis: Trends and Perspectives Towards Industry 4.0","volume":"50","author":"Galar","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1109\/TII.2014.2349359","article-title":"Machine Learning for Predictive Maintenance: A Multiple Classifier Approach","volume":"11","author":"Susto","year":"2015","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1109\/TSM.2012.2209131","article-title":"A Predictive Maintenance System for Epitaxy Processes Based on Filtering and Prediction Techniques","volume":"25","author":"Susto","year":"2012","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"23484","DOI":"10.1109\/ACCESS.2017.2765544","article-title":"Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance","volume":"5","author":"Yan","year":"2017","journal-title":"IEEE Access"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"071018","DOI":"10.1115\/1.4036350","article-title":"A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests","volume":"139","author":"Wu","year":"2017","journal-title":"ASME J. Manuf. Sci. Eng."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5795037","DOI":"10.1155\/2018\/5795037","article-title":"SVM-Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0","volume":"2018","author":"Shin","year":"2018","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.sysarc.2017.10.007","article-title":"Automatic Machine Status Prediction in the Era of Industry 4.0: Case Study of Machines in a Spring Factory","volume":"81","author":"Kuo","year":"2017","journal-title":"J. Syst. Archit."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MCC.2018.1081065","article-title":"A MapReduce-Based Ensemble Learning Method with Multiple Classifier Types and Diversity for Condition-based Maintenance with Concept Drifts","volume":"4","author":"Lin","year":"2017","journal-title":"IEEE Cloud Comput."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1109\/TKDE.2011.58","article-title":"DDD: A New Ensemble Approach for Dealing with Concept Drift","volume":"24","author":"Minku","year":"2012","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TPDS.2015.2419671","article-title":"On Traffic-Aware Partition and Aggregation in MapReduce for Big Data Applications","volume":"27","author":"Ke","year":"2016","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Yu, W., Dillon, T.S., Mostafa, F., Rahayu, W., and Liu, Y. (2019). A Global Manufacturing Big Data Ecosystem for Fault Detection in Predictive Maintenance. IEEE Trans. Ind. Inf.","DOI":"10.1109\/TII.2019.2915846"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.compind.2018.07.004","article-title":"IDARTS\u2014Towards Intelligent Data Analysis and Real-Time Supervision for Industry 4.0","volume":"101","author":"Peres","year":"2018","journal-title":"Comput. Ind."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"17190","DOI":"10.1109\/ACCESS.2018.2809681","article-title":"Industrial Big Data Analytics for Prediction of Remaining Useful Life Based on Deep Learning","volume":"6","author":"Yan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2416","DOI":"10.1109\/TII.2018.2881543","article-title":"Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing","volume":"15","author":"Sun","year":"2019","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1109\/TII.2018.2866549","article-title":"Machine Health Monitoring Using Adaptive Kernel Spectral Clustering and Deep Long Short-Term Memory Recurrent Neural Networks","volume":"15","author":"Cheng","year":"2019","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_76","first-page":"3794","article-title":"Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultra-Precision Manufacturing","volume":"66","author":"Shi","year":"2019","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Karnouskos, S. (2011, January 7\u201310). Stuxnet Worm Impact on Industrial Cyber-Physical System Security. Proceedings of the IECON 2011\u201437th Annual Conference of the IEEE Industrial Electronics Society, Melbourne, Australia.","DOI":"10.1109\/IECON.2011.6120048"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s41635-017-0031-0","article-title":"Manufacturing Supply Chain and Product Lifecycle Security in the Era of Industry 4.0","volume":"2","author":"Chhetri","year":"2018","journal-title":"J. Hardware Syst. Secur."},{"key":"ref_79","unstructured":"Hankel, M., and Rexroth, B. (2019, December 22). The Reference Architectural Model Industrie 4.0 (RAMI 4.0). Available online: https:\/\/ec.europa.eu\/futurium\/en\/system\/files\/ged\/a2-schweichhart-reference_architectural_model_industrie_4.0_rami_4.0.pdf."},{"key":"ref_80","unstructured":"Industrial Internet Consortium (2019, December 22). Industrial Internet Reference Architecture (IIRA). Available online: http:\/\/www.iiconsortium.org."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"32910","DOI":"10.1109\/ACCESS.2018.2844794","article-title":"A New Threat Intelligence Scheme for Safeguarding Industry 4.0 Systems","volume":"6","author":"Moustafa","year":"2018","journal-title":"IEEE Access"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1109\/TBDATA.2017.2715166","article-title":"Novel Geometric Area Analysis Technique for Anomaly Detection Using Trapezoidal Area Estimation on Large-Scale Networks","volume":"5","author":"Moustafa","year":"2019","journal-title":"IEEE Trans. Big Data"},{"key":"ref_83","unstructured":"Helske, S., and Helske, J. (2017). Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R. arXiv."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/S0893-6080(00)00026-5","article-title":"Independent Component Analysis: Algorithms and Applications","volume":"13","author":"Oja","year":"2000","journal-title":"Neural Netw."},{"key":"ref_85","unstructured":"Hink, R.C.B., Beaver, J.M., Buckner, M.A., Morris, T., Adhikari, U., and Pan, S. (2014, January 19\u201321). Machine Learning for Power System Disturbance and Cyber-Attack Discrimination. Proceedings of the International Symposium on Resilient Control Systems (ISRCS), Denver, CO, USA."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Moustafa, N., and Slay, J. (2015, January 10\u201312). UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection. Proceedings of the 2015 Military Communications and Information Systems Conference (MilCIS), Canberra, Australia.","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1007\/s10845-017-1315-5","article-title":"Detecting Cyber-Physical Attacks in CyberManufacturing Systems with Machine Learning Methods","volume":"30","author":"Wu","year":"2019","journal-title":"J. Intell. Manuf."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Park, S.T., Li, G., and Hong, J.C. (2018). A Study on Smart Factory-Based Ambient Intelligence Context-Aware Intrusion Detection System Using Machine Learning. J. Ambient Intell. Humaniz. Comput., 1\u20138.","DOI":"10.1007\/s12652-018-0998-6"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Keliris, A., Salehghaffari, H., Cairl, B., Krishnamurthy, P., Maniatakos, M., and Khorrami, F. (2016, January 15\u201317). Machine Learning-Based Defense Against Process-Aware Attacks on Industrial Control Systems. Proceedings of the 2016 IEEE International Test Conference (ITC), Fort Worth, TX, USA.","DOI":"10.1109\/TEST.2016.7805855"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/0098-1354(93)80018-I","article-title":"A Plant-Wide Industrial Process Control Problem","volume":"17","author":"Downs","year":"1993","journal-title":"Comput. Chem. Eng."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.eng.2018.01.002","article-title":"Toward New-Generation Intelligent Manufacturing","volume":"4","author":"Zhou","year":"2018","journal-title":"Engineering"},{"key":"ref_92","first-page":"10","article-title":"Industrial IoT in 5G Environment Towards Smart Manufacturing","volume":"10","author":"Cheng","year":"2018","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"70891","DOI":"10.1109\/ACCESS.2019.2919340","article-title":"The Intelligent Factory Space\u2014A Concept for Observing, Learning and Communicating in the Digitalized Factory","volume":"7","author":"Reimann","year":"2019","journal-title":"IEEE Access"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.jmsy.2018.04.019","article-title":"Patented Intelligence: Cloning Human Decision Models for Industry 4.0","volume":"48","author":"Terziyan","year":"2018","journal-title":"J. Manuf. Syst."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Nahavandi, S. (2019). Industry 5.0\u2014A Human-Centric Solution. Sustainability, 11.","DOI":"10.3390\/su11164371"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1016\/S1569-190X(03)00094-7","article-title":"Humans: The Missing Link in Manufacturing Simulation?","volume":"12","author":"Baines","year":"2004","journal-title":"Simul. Modell. Pract. Theory"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.promfg.2018.04.003","article-title":"Rethinking Human-Machine Learning in Industry 4.0: How Does the Paradigm Shift Treat the Role of Human Learning?","volume":"23","author":"Ansari","year":"2018","journal-title":"Procedia Manuf."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"10434","DOI":"10.1109\/ACCESS.2017.2706739","article-title":"Analytical Modeling of Human Choice Complexity in a Mixed Model Assembly Line Using Machine Learning-Based Human in the Loop Simulation","volume":"5","author":"Busogi","year":"2017","journal-title":"IEEE Access"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1109\/THMS.2017.2717885","article-title":"A Symbiotic Human\u2013Machine Learning Approach for Production Ramp-up","volume":"48","author":"Doltsinis","year":"2018","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Zheng, X., Wang, M., and Ordieres-Mer\u00e9, J. (2018). Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0. Sensors, 18.","DOI":"10.3390\/s18072146"},{"key":"ref_101","unstructured":"(2019, December 22). Deep Learning & Artificial Intelligence Solutions from NVIDIA. Available online: https:\/\/www.nvidia.com\/en-us\/deep-learning-ai\/."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Nurvitadhi, E., Venkatesh, G., Sim, J., Marr, D., Huang, R., Ong Gee Hock, J., Liew, Y.T., Srivatsan, K., Moss, D., and Subhaschandra, S. (2017, January 22\u201324). Can FPGAs beat GPUs in Accelerating Next-Generation Deep Neural Networks?. Proceedings of the ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, Monterey, CA, USA.","DOI":"10.1145\/3020078.3021740"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Lesjak, C., Hein, D., and Winter, J. (2015, January 9\u201312). Hardware-Security Technologies for Industrial IoT: TrustZone and Security Controller. Proceedings of the IECON 2015\u201441st Annual Conference of the IEEE Industrial Electronics Society, Yokohama, Japan.","DOI":"10.1109\/IECON.2015.7392493"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/109\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:45:00Z","timestamp":1760190300000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/109"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,23]]},"references-count":103,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20010109"],"URL":"https:\/\/doi.org\/10.3390\/s20010109","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,23]]}}}