Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects
Open accessAbstract
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–machine 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.
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750 · see citing works
Independent component analysis: algorithms and applications
Hyvärinen, A., & Erkki Oja, A. (2000) · Neural Networks · doi:10.1016/s0893-6080(00)00026-5 · Cited by 8,726 · see citing works
Moustafa, N., & Slay, J. (2015) · 2015 Military Communications and Information Systems Conference (MilCIS) · doi:10.1109/milcis.2015.7348942 · Cited by 4,033 · see citing works
Industry 4.0: state of the art and future trends
Xu, L. D., Xu, E. L., & Li, L. (2018) · International Journal of Production Research · doi:10.1080/00207543.2018.1444806 · Cited by 3,405 · see citing works
A plant-wide industrial process control problem
Downs, J. J., & Vogel, E. F. (1993) · Computers & Chemical Engineering · doi:10.1016/0098-1354(93)80018-i · Cited by 3,348 · see citing works
Intelligent Manufacturing in the Context of Industry 4.0: A Review
Zhong, R. Y., Xu, X. W., Klotz, E., & Newman, S. T. (2017) · Engineering · doi:10.1016/j.eng.2017.05.015 · Cited by 3,045 · see citing works
Industry 4.0: A survey on technologies, applications and open research issues
Lu, Y. (2017) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2017.04.005 · Cited by 3,012 · see citing works
Literature review of Industry 4.0 and related technologies
Oztemel, E., & Gursev, S. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-018-1433-8 · Cited by 2,143 · see citing works
Opportunities of Sustainable Manufacturing in Industry 4.0
Stock, T. W., & Seliger, G. (2016) · Procedia CIRP · doi:10.1016/j.procir.2016.01.129 · Cited by 1,947 · see citing works
Data-driven smart manufacturing
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.01.006 · Cited by 1,934 · see citing works
Deep learning for smart manufacturing: Methods and applications
Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.01.003 · Cited by 1,740 · see citing works
Industry 5.0—A Human-Centric Solution
Nahavandi, S. (2019) · Sustainability · doi:10.3390/su11164371 · Cited by 1,718 · see citing works
Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison
Qi, Q., & Tao, F. (2018) · IEEE Access · doi:10.1109/access.2018.2793265 · Cited by 1,716 · see citing works
Cyber-physical systems in manufacturing
Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S. R. T., Reinhart, G., Sauer, O., Schuh, G., Sihn, W., & Ueda, K. (2016) · CIRP Annals · doi:10.1016/j.cirp.2016.06.005 · Cited by 1,708 · see citing works
Machine learning in manufacturing: Advantages, challenges, and applications
Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D. (2016) · Production & Manufacturing Research · doi:10.1080/21693277.2016.1192517 · Cited by 1,319 · see citing works
Lei, Y., Jia, F., Lin, J., Xing, S., & Ding, S. X. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2519325 · Cited by 1,191 · see citing works
Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges
Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M., & Yin, B. (2017) · IEEE Access · doi:10.1109/access.2017.2783682 · Cited by 1,150 · see citing works
A Categorical Framework of Manufacturing for Industry 4.0 and Beyond
Qin, J., Liu, Y., & Grosvenor, R. I. (2016) · Procedia CIRP · doi:10.1016/j.procir.2016.08.005 · Cited by 1,043 · see citing works
Data Mining and Analytics in the Process Industry: The Role of Machine Learning
Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017) · IEEE Access · doi:10.1109/access.2017.2756872 · Cited by 922 · cited 6× · see citing works
“• Unsupervised learning where there is no feedback provided from anyone and the algorithm finds patterns in unknown data sets (clustering, association rules, self-organised maps) [15,19,21] and so, unlabeled data are used for training purposes.”
BackgroundMethodInfluentialOptimization method based extreme learning machine for classification
Huang, G.-B., Ding, X., & Zhou, H. (2010) · Neurocomputing · doi:10.1016/j.neucom.2010.02.019 · Cited by 870 · see citing works
Applications of artificial intelligence in intelligent manufacturing: a review
Li, B.-H., Hou, B., Yu, W., Lu, X., & Yang, C. (2017) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1601885 · Cited by 828 · see citing works
Machine Learning for Predictive Maintenance: A Multiple Classifier Approach
Susto, G. A., Schirru, A., Pampuri, S., McLoone, S., & Beghi, A. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2349359 · Cited by 763 · see citing works
Wu, D., Jennings, C., Terpenny, J. P., Gao, R. X., & Kumara, S. R. T. (2017) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4036350 · Cited by 688 · see citing works
Toward New-Generation Intelligent Manufacturing
Zhou, J., Li, P., Zhou, Y., et al. (2018) · Engineering · doi:10.1016/j.eng.2018.01.002 · Cited by 645 · see citing works
A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective
Xu, H., Yu, W., Griffith, D., & Golmie, N. (2018) · IEEE Access · doi:10.1109/access.2018.2884906 · Cited by 637 · see citing works
Diez-Olivan, A., Del Ser, J., Galar, D., & Sierra, B. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.10.005 · Cited by 633 · see citing works
Deploying Fog Computing in Industrial Internet of Things and Industry 4.0
Aazam, M., Zeadally, S., & Harras, K. A. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2855198 · Cited by 572 · see citing works
Industry 4.0: A bibliometric analysis and detailed overview
Muhuri, P. K., Shukla, A. K., & Abraham, A. (2018) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2018.11.007 · Cited by 550 · see citing works
Stuxnet worm impact on industrial cyber-physical system security
Karnouskos, S. (2011) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2011.6120048 · Cited by 542 · see citing works
Big data for cyber physical systems in industry 4.0: a survey
Xu, L. D., & Duan, L. (2018) · Enterprise Information Systems · doi:10.1080/17517575.2018.1442934 · Cited by 530 · see citing works
Sun, C., Ma, M., Zhao, Z., Tian, S., Yan, R., & Chen, X. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2881543 · Cited by 519 · see citing works
Yan, J., Meng, Y., Lü, L., & Lin, L. (2017) · IEEE Access · doi:10.1109/access.2017.2765544 · Cited by 479 · see citing works
Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?
Nurvitadhi, E., Venkatesh, G., Sim, J., Marr, D., Huang, R., Ong Gee Hock, J., Liew, Y. T., Srivatsan, K., Moss, D., Subhaschandra, S., et al. (2017) · Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays · doi:10.1145/3020078.3021740 · Cited by 467 · see citing works
Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing
Li, L., Ota, K., & Dong, M. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2842821 · Cited by 466 · see citing works
Industrial IoT in 5G environment towards smart manufacturing
Cheng, J., Chen, W., Tao, F., & Lin, C. (2018) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2018.04.001 · Cited by 462 · see citing works
DDD: A New Ensemble Approach for Dealing with Concept Drift
Minku, L. L., & Yao, X. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2011.58 · Cited by 441 · see citing works
Syafrudin, M., Alfian, G., Fitriyani, N. L., & Rhee, J. (2018) · Sensors · doi:10.3390/s18092946 · Cited by 401 · see citing works
The role of big data analytics in industrial Internet of Things
Rehman, M. H. U., Yaqoob, I., Salah, K., Imran, M. A., Jayaraman, P. P., & Perera, C. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.04.020 · Cited by 373 · see citing works
A review of machine learning for the optimization of production processes
Weichert, D., Link, P., Stoll, A., Stefan Rüping, A., Ihlenfeldt, S., & Wrobel, S. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-03988-5 · Cited by 364 · see citing works
Trappey, A. J. C., Trappey, C. V., Govindarajan, U. H., Chuang, A. C., & Sun, J. J. H. (2016) · Advanced Engineering Informatics · doi:10.1016/j.aei.2016.11.007 · Cited by 358 · see citing works
Artificial Intelligence-Driven Mechanism for Edge Computing-Based Industrial Applications
Sodhro, A. H., Pirbhulal, S., & de Albuquerque, V. H. C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2902878 · Cited by 348 · see citing works
Machine learning for power system disturbance and cyber-attack discrimination
Hink, R. B., Beaver, J. M., Buckner, M. A., Morris, T., Adhikari, U., & Pan, S. (2014) · International Symposium on Resilient Control Systems (ISRCS) · doi:10.1109/isrcs.2014.6900095 · Cited by 325 · see citing works
Moustafa, N., Slay, J., & Creech, G. (2017) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2017.2715166 · Cited by 320 · see citing works
Classification of Imbalanced Data by Oversampling in Kernel Space of Support Vector Machines
Mathew, J., Pang, C. K., Luo, M., et al. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2751612 · Cited by 314 · see citing works
Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification
Luo, B., Wang, H., Liu, H., Li, B., & Peng, F. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2807414 · Cited by 308 · see citing works
Reis, M. S., & Gins, G. (2017) · Processes · doi:10.3390/pr5030035 · Cited by 296 · see citing works
Pan, J. H., Zi, Y., Chen, J., Zhou, Z., & Wang, B. (2017) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2767540 · Cited by 295 · see citing works
Natural computing for mechanical systems research: A tutorial overview
Worden, K., Staszewski, W. J., & Hensman, J. J. (2010) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2010.07.013 · Cited by 290 · see citing works
A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing
Wu, D., Liu, S., Zhang, L., Terpenny, J. P., Gao, R. X., Kurfess, T. R., & Guzzo, J. A. (2017) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2017.02.011 · Cited by 289 · see citing works
Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry
Kim, D.-H., Kim, T. J. Y., Wang, X., Kim, M., Quan, Y., Oh, J. W., Min, S., Kim, H., Bhandari, B., Yang, I., et al. (2018) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-018-0057-y · Cited by 287 · see citing works
Data and knowledge mining with big data towards smart production
Cheng, Y., Chen, K., Sun, H., Zhang, Y., & Tao, F. (2017) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2017.08.001 · Cited by 275 · see citing works
Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing
Moyne, J. R., & Iskandar, J. (2017) · Processes · doi:10.3390/pr5030039 · Cited by 259 · see citing works
Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods
Wu, M., Song, Z., & Moon, Y. B. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-017-1315-5 · Cited by 238 · see citing works
Enabling Technologies for Operator 4.0: A Survey
Ruppert, T., Jaskó, S., Holczinger, T., & Abonyi, J. (2018) · Applied Sciences · doi:10.3390/app8091650 · Cited by 236 · see citing works
A global manufacturing big data ecosystem for fault detection in predictive maintenance
Yu, W., Dillon, T., Mostafa, F., Rahayu, W., & Liu, Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2915846 · Cited by 225 · see citing works
Cheng, Y., Zhu, H., Wu, J., & Shao, X. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2866549 · Cited by 218 · see citing works
Fault Detection and Isolation in Industrial Processes Using Deep Learning Approaches
Iqbal, R., Maniak, T., Doctor, F., & Karyotis, C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2902274 · Cited by 218 · see citing works
A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis
Sohaib, M., Kim, C. H., & Kim, J.-M. (2017) · Sensors · doi:10.3390/s17122876 · Cited by 202 · see citing works
IDARTS – Towards intelligent data analysis and real-time supervision for industry 4.0
Peres, R. S., Rocha, A. D., Leitão, P., & Barata, J. (2018) · Computers in Industry · doi:10.1016/j.compind.2018.07.004 · Cited by 197 · see citing works
The Tactile Internet for Industries: A Review
Aijaz, A., & Sooriyabandara, M. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2878265 · Cited by 169 · see citing works
Ramotsoela, D. T., Abu-Mahfouz, A. M. I., & Hancke, G. P. (2018) · Sensors · doi:10.3390/s18082491 · Cited by 158 · see citing works
A New Threat Intelligence Scheme for Safeguarding Industry 4.0 Systems
Moustafa, N., Adi, E., Turnbull, B., & Hu, J. (2018) · IEEE Access · doi:10.1109/access.2018.2844794 · Cited by 153 · see citing works
Zheng, X., Wang, M., & Ordieres-Meré, J. B. (2018) · Sensors · doi:10.3390/s18072146 · Cited by 145 · see citing works
Ansari, F., Erol, S., & Sihn, W. (2018) · Procedia Manufacturing · doi:10.1016/j.promfg.2018.04.003 · Cited by 144 · see citing works
O’Donovan, P., Gallagher, C., Bruton, K., & O’Sullivan, D. T. J. (2018) · Manufacturing Letters · doi:10.1016/j.mfglet.2018.01.005 · Cited by 140 · see citing works
Artificial Intelligence for Cloud-Assisted Smart Factory
Wan, J., Yang, J., Wang, Z., & Hua, Q. (2018) · IEEE Access · doi:10.1109/access.2018.2871724 · Cited by 139 · see citing works
Shi, C., Panoutsos, G., Luo, B., et al. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2856193 · Cited by 138 · see citing works
Industrial Big Data Analysis in Smart Factory: Current Status and Research Strategies
Xu, X., & Hua, Q. (2017) · IEEE Access · doi:10.1109/access.2017.2741105 · Cited by 136 · see citing works
Industrial Big Data Analytics for Prediction of Remaining Useful Life Based on Deep Learning
Yan, H., Wan, J., Zhang, C., Tang, S., Hua, Q., & Wang, Z. (2018) · IEEE Access · doi:10.1109/access.2018.2809681 · Cited by 125 · see citing works
Humans: the missing link in manufacturing simulation?
Baines, T. S., Mason, S. R., Siebers, P., & Ladbrook, J. (2003) · Simulation Modelling Practice and Theory · doi:10.1016/s1569-190x(03)00094-7 · Cited by 123 · see citing works
A Predictive Maintenance System for Epitaxy Processes Based on Filtering and Prediction Techniques
Susto, G. A., Beghi, A., & Luca, C. D. (2012) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2012.2209131 · Cited by 105 · see citing works
Framework and development of fault detection classification using IoT device and cloud environment
Lee, H. (2017) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2017.02.007 · Cited by 103 · see citing works
Ye, F., Zhang, Z., Chakrabarty, K., et al. (2013) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2012.2234827 · Cited by 101 · see citing works
Park, S.-T., Li, G., & Hong, J.-C. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0998-6 · Cited by 97 · see citing works
Patented intelligence: Cloning human decision models for Industry 4.0
Terziyan, V. Y., Gryshko, S., & Golovianko, M. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.04.019 · Cited by 96 · see citing works
Lin, C., Deng, D., Kuo, C.-H., & Chen, L. (2019) · IEEE Access · doi:10.1109/access.2019.2912631 · Cited by 94 · see citing works
Yang, Z.-X., Wang, X., & Zhong, J. (2016) · Energies · doi:10.3390/en9060379 · Cited by 84 · see citing works
Machine learning-based defense against process-aware attacks on Industrial Control Systems
Keliris, A., Salehghaffari, H., Cairl, B., Krishnamurthy, P., Maniatakos, M., & Khorrami, F. (2016) · IEEE International Test Conference (ITC) · doi:10.1109/test.2016.7805855 · Cited by 81 · see citing works
Artificial intelligence/machine learning in manufacturing and inspection: A GE perspective
Aggour, K. S., Gupta, V. K., Ruscitto, D. M., Ajdelsztajn, L., Bian, X., Brosnan, K. H., Kumar, N. C., Dheeradhada, V. S., Hanlon, T., Iyer, N., et al. (2019) · MRS Bulletin · doi:10.1557/mrs.2019.157 · Cited by 70 · see citing works
Hardware-security technologies for industrial IoT: TrustZone and security controller
Lesjak, C., Hein, D. M., & Winter, J. (2015) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2015.7392493 · Cited by 65 · see citing works
Performance of Machine Learning Algorithms for Class-Imbalanced Process Fault Detection Problems
Lee, T., Lee, K. B., & Kim, C. O. (2016) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2016.2602226 · Cited by 65 · see citing works
Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT
Lavassani, M., Forsström, S., Jennehag, U., & Zhang, T. (2018) · Sensors · doi:10.3390/s18051532 · Cited by 65 · see citing works
Manufacturing Supply Chain and Product Lifecycle Security in the Era of Industry 4.0
Chhetri, S. R., Faezi, S., Rashid, N., & Faruque, M. A. A. (2017) · Journal of Hardware and Systems Security · doi:10.1007/s41635-017-0031-0 · Cited by 63 · see citing works
Machine Learning-based CPS for Clustering High throughput Machining Cycle Conditions
Díaz-Rozo, J., Bielza, C., & Larrañaga, P. (2017) · Procedia Manufacturing · doi:10.1016/j.promfg.2017.07.091 · Cited by 55 · see citing works
Trakadas, P. T., Νομικός, Ν., Michailidis, E. T., et al. (2019) · Sensors · doi:10.3390/s19163591 · Cited by 53 · see citing works
Self-citationOn Traffic-Aware Partition and Aggregation in MapReduce for Big Data Applications
Ke, H., Li, P., Guo, S., et al. (2015) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2015.2419671 · Cited by 46 · see citing works
Sonntag, D., Zillner, S., van der Smagt, P., & Lörincz, A. (2016) · Springer series in wireless technology · doi:10.1007/978-3-319-42559-7_19 · Cited by 43 · see citing works
A New Snapshot Ensemble Convolutional Neural Network for Fault Diagnosis
Wen, L., Gao, L., & Li, X. (2019) · IEEE Access · doi:10.1109/access.2019.2903295 · Cited by 42 · see citing works
Kuo, C.-J., Ting, K.-C., Chen, Y., Yang, D., & Chen, H. (2017) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2017.10.007 · Cited by 41 · see citing works
Spur Gear Fault Diagnosis Using a Multilayer Gated Recurrent Unit Approach With Vibration Signal
Tao, Y., Wang, X., Sánchez, R., Yang, S., & Bai, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2914181 · Cited by 41 · see citing works
Efficient Board-Level Functional Fault Diagnosis With Missing Syndromes
Jin, S., Ye, F., Zhang, Z., et al. (2015) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2015.2481859 · Cited by 39 · see citing works
SVM‐Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0
Shin, H.-J., Cho, K.-W., & Oh, C.-H. (2018) · Wireless Communications and Mobile Computing · doi:10.1155/2018/5795037 · Cited by 39 · see citing works
A Symbiotic Human–Machine Learning Approach for Production Ramp-up
Doltsinis, S. C., Ferreira, P., & Lohse, N. (2017) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2017.2717885 · Cited by 26 · see citing works
Traffic-Aware Partition and Aggregation in Map Reduce for Big Data Applications
Supriya, M. (2018) · Cited by 26 · see citing works
Maier, A., Schriegel, S., & Niggemann, O. (2016) · Springer series in wireless technology · doi:10.1007/978-3-319-42559-7_18 · Cited by 22 · see citing works
Reimann, J., & Sziebig, G. (2019) · IEEE Access · doi:10.1109/access.2019.2919340 · Cited by 22 · see citing works
Busogi, M., & Kim, N. (2017) · IEEE Access · doi:10.1109/access.2017.2706739 · Cited by 14 · see citing works
Lin, C., Shu, L., Deng, D., et al. (2017) · IEEE Cloud Computing · doi:10.1109/mcc.2018.1081065 · Cited by 10 · see citing works
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Research works printed but not linked: 1
- [83]Helske S. Helske J. Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R arXiv 2017 1704.00543 10.18637/jss.v088.i03 · DOI
Cited by
Sorted by citations
A Review of Cybersecurity Guidelines for Manufacturing Factories in Industry 4.0
Mullet, V., Sondi, P., & Ramat, E. (2021) · IEEE Access · doi:10.1109/access.2021.3056650 · Cited by 161 · see citing works
“Among the most recent surveys, [8] also investigated machine learning solutions for tackling faults in the Industry 4.0 era.”
BackgroundInfluentialKey Challenges and Emerging Technologies in Industrial IoT Architectures: A Review
Mirani, A. A., Velasco-Hernandez, G., Awasthi, A., & Walsh, J. (2022) · Sensors · doi:10.3390/s22155836 · Cited by 119 · see citing works
- Authors
- Angelopoulos, A., Michailidis, E. T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., & Zahariadis, T.
- Affiliations
- National and Kapodistrian University of Athens (GR)
- University of West Attica (GR)
- Department of Information and Communication Systems Engineering, School of Engineering, University of the Aegean, 83200 Samos, Greece
- General Department, National and Kapodistrian University of Athens, Thesi skliro, Psahna, 34400 Evia, Greece
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-12-23
- Volume
- 20
- Issue
- 1
- Pages
- 109
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6983262
- OpenAlex
- W2995450656
- Cited by
- 365
- References
- 99
- Citations by year
- 2026: 29 · 2025: 60 · 2024: 85 · 2023: 65 · 2022: 60 · 2021: 53 · 2020: 12 · 2019: 1
- FWCI
- 3.11
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- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
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- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- Industry 4.0, fault detection, fault prediction, fault prevention, machine learning, Industrial Internet of Things, industrial cybersecurity, human-machine interaction, human-in-the-loop, process automation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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