Multi-agent system for anomaly detection in Industry 4.0 using Machine Learning techniques
Abstract Industry 4.0 is the new industrial stage that is committed to greater automation, connectivity and globalization. The interrelation between the different areas has penetrated the industrial world thanks to the Internet of things and the world of Big Data. This amount of information available in plants is growing increasingly, also aided by the network computing services offered by cloud computing or edge computing. That is why it’s necessary to carry out complex fusion methods and data analysis using Machine Learning techniques to address specific industrial requirements and needs. The central challenge of industry 4.0 from the perspective of data science is to predict the history within the monitored processes, providing as much information as possible, avoiding them and stave off severe economic losses. This article will show a review of the application of Artificial Intelligence (AI) techniques such as Machine Learning (ML) immersed in multi-agent systems (MAS) in Industry 4.0. For this, a bibliographic search has been carried out in databases recognized as Science Direct, Google Scholar, Scopus or Springer, filtering the investigations from 2018 to the actually. The article concludes by pointing out the possible future lines and the importance of the transition towards the implementation of new technologies for the competitiveness of factories.
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Shafiee, M. and Sørensen, J. D., 2019. Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies. Reliability Engineering and System Safety, 192:105993. ISSN 0951-8320. doi:https://doi.org/10.1016/j.ress.2017.10.025. Complex Systems RAMS Optimization: Methods and Applications. - https://doi.org/10.1016/j.ress.2017.10.025
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Wolfert, S., Ge, L., Verdouw, C., and Bogaardt, M.-J., 2017. Big Data in Smart Farming-A review. Agricultural Systems, 153:69-80. ISSN 0308-521X. doi:https://doi.org/10.1016/j.agsy.2017.01.023. - https://doi.org/10.1016/j.agsy.2017.01.023
Zhang, H., Chen, H., Guo, Y., Wang, J., Li, G., and Shen, L., 2019. Sensor fault detection and diagnosis for a water source heat pump air-conditioning system based on PCA and preprocessed by combined clustering. Applied Thermal Engineering, 160:114098. ISSN 1359-4311. doi:https://doi.org/10.1016/j.applthermaleng. 2019.114098. - https://doi.org/10.1016/j.applthermaleng.2019.114098
Zheng, J., Wang, H., Song, Z., and Ge, Z., 2019. Ensemble semi-supervised Fisher discriminant analysis model for fault classification in industrial processes. ISA Transactions, 92:109-117. ISSN 0019-0578. doi:https://doi.org/10.1016/j.isatra.2019.02.021. - https://doi.org/10.1016/j.isatra.2019.02.021
Cabrera, D., Guamán, A., Zhang, S., Cerrada, M., Sánchez, R.-V., Cevallos, J., Long, J., and Li, C., 2020. Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor. Neurocomputing, 380:51-66. ISSN 0925-2312. doi:https: //doi.org/10.1016/j.neucom.2019.11.006. - https://doi.org/10.1016/j.neucom.2019.11.006
Carvalho, A., Mahony, N. O., Krpalkova, L., Campbell, S., Walsh, J., and Doody, P., 2019. At the Edge of Industry 4.0. Procedia Computer Science, 155:276-281. ISSN 1877-0509. doi:https://doi.org/10.1016/j.procs. 2019.08.039. The 16th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2019),The 14th International Conference on Future Networks and Communications (FNC-2019),The 9th International Conference on Sustainable Energy Information Technology. - https://doi.org/10.1016/j.procs.2019.08.039
Ceruti, A., Marzocca, P., Liverani, A., and Bil, C., 2019. Maintenance in aeronautics in an Industry 4.0 context: The role of Augmented Reality and Additive Manufacturing. Journal of Computational Design and Engineering, 6(4):516-526. ISSN 2288-4300. doi:https://doi.org/10.1016/j.jcde.2019.02.001. - https://doi.org/10.1016/j.jcde.2019.02.001
Chen, H., Jiang, B., Zhang, T., and Lu, N., 2019. Data-driven and deep learning-based detection and diagnosis of incipient faults with application to electrical traction systems. Neurocomputing. ISSN 0925-2312. doi:https://doi.org/10.1016/j.neucom.2018.07.103. - https://doi.org/10.1016/j.neucom.2018.07.103
Diez-Olivan, A., Ser, J. D., Galar, D., and Sierra, B., 2019. Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0. Information Fusion, 50:92-111. - https://doi.org/10.1016/j.inffus.2018.10.005
Han, J., Kamber, M., and Pei, J., 2012. 3 - Data Preprocessing. In Data Mining (Third Edition), The Morgan Kaufmann Series in Data Management Systems, pages 83-124. Morgan Kaufmann, Boston, third edition edition. ISBN 978-0-12-381479-1. doi:https://doi.org/10.1016/B978-0-12-381479-1.00003-4. - https://doi.org/10.1016/B978-0-12-381479-1.00003-4
Hanga, K. M. and Kovalchuk, Y., 2019. Machine learning and multi-agent systems in oil and gas industry applications: A survey. Computer Science Review, 34:100191. ISSN 1574-0137. doi:https://doi.org/10. 1016/j.cosrev.2019.08.002. - https://doi.org/10.1016/j.cosrev.2019.08.002
Hasan, M. J., Islam, M. M., and Kim, J.-M., 2019. Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions. Measurement, 138:620-631. ISSN 0263-2241. doi:https://doi.org/10.1016/j.measurement.2019.02.075. - https://doi.org/10.1016/j.measurement.2019.02.075
Hernandez-Leal, P., Kartal, B., and Taylor, M. E., 2019. A survey and critique of multiagent deep reinforcement learning. Autonomous Agents and Multi-Agent Systems, 33:750-797. - https://doi.org/10.1007/s10458-019-09421-1
Lade, P., Ghosh, R., and Srinivasan, S., 2017. Manufacturing Analytics and Industrial Internet of Things. IEEE Intelligent Systems, 32:74-79. - https://doi.org/10.1109/MIS.2017.49
Liu, Q., Dong, M., Chen, F., Lv, W., and Ye, C., 2019. Single-machine-based joint optimization of predictive maintenance planning and production scheduling. Robotics and Computer-Integrated Manufacturing, 55:173-182. ISSN 0736-5845. doi:https://doi.org/10.1016/j.rcim.2018.09.007. Extended Papers Selected from FAIM2016. - https://doi.org/10.1016/j.rcim.2018.09.007
Luo, X., Fong, K., Sun, Y., and Leung, M., 2019. Development of clustering-based sensor fault detection and diagnosis strategy for chilled water system. Energy and Buildings, 186:17-36. ISSN 0378-7788. doi:https://doi.org/10.1016/j.enbuild.2019.01.006. - https://doi.org/10.1016/j.enbuild.2019.01.006
Ma, S., Cheng, B., Shang, Z., and Liu, G., 2018. Scattering transform and LSPTSVM based fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 104:155-170. ISSN 0888-3270. doi:https://doi.org/10.1016/j.ymssp.2017.10.026. - https://doi.org/10.1016/j.ymssp.2017.10.026
Martins, H., Januario, F., Brito Palma, L., Cardoso, A., and Gil, P., 2015. A machine learning technique in a multi-agent framework for online outliers detection in Wireless Sensor Networks. pages 000688-000693. doi:10.1109/IECON.2015.7392180. - https://doi.org/10.1109/IECON.2015.7392180
Pang, S., Yang, X., Zhang, X., and Lin, X., 2019. Fault diagnosis of rotating machinery with ensemble kernel extreme learning machine based on fused multi-domain features. ISA Transactions. ISSN 0019-0578. doi:https://doi.org/10.1016/j.isatra.2019.08.053. - https://doi.org/10.1016/j.isatra.2019.08.053
Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., and Loncarski, J., 2018. Machine Learning approach for Predictive Maintenance in Industry 4.0. pages 1-6. doi:10.1109/MESA.2018.8449150. - https://doi.org/10.1109/MESA.2018.8449150
Peres, R. S., Rocha, A. D., Leitao, P., and Barata, J., 2018. IDARTS-Towards intelligent data analysis and real-time supervision for industry 4.0. Computers in Industry, 101:138-146. ISSN 0166-3615. doi:https://doi.org/10.1016/j.compind.2018.07.004. - https://doi.org/10.1016/j.compind.2018.07.004
Ramchandran, A. and Sangaiah, A. K., 2018. Chapter 11 - Unsupervised Anomaly Detection for High Dimensional Data-an Exploratory Analysis. In Sangaiah, A. K., Sheng, M., and Zhang, Z., editors, Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, Intelligent Data-Centric Systems, pages 233-251. Academic Press. ISBN 978-0-12-813314-9. doi: https://doi.org/10.1016/B978-0-12-813314-9.00011-6. - https://doi.org/10.1016/B978-0-12-813314-9.00011-6
Ruiz-Sarmiento, J.-R., Monroy, J., Moreno, F.-A., Galindo, C., Bonelo, J.-M., and Gonzalez-Jimenez, J., 2020. A predictive model for the maintenance of industrial machinery in the context of industry 4.0. Engineering Applications of Artificial Intelligence, 87:103289. ISSN 0952-1976. doi:https://doi.org/10.1016/j.engappai. 2019.103289. - https://doi.org/10.1016/j.engappai.2019.103289
Shafiee, M. and Sørensen, J. D., 2019. Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies. Reliability Engineering and System Safety, 192:105993. ISSN 0951-8320. doi:https://doi.org/10.1016/j.ress.2017.10.025. Complex Systems RAMS Optimization: Methods and Applications. - https://doi.org/10.1016/j.ress.2017.10.025
Tsang, C.-H. and Kwong, S., 2006. Multi-agent intrusion detection system in industrial network using ant colony clustering approach and unsupervised feature extraction. pages 51-56. ISBN 0-7803-9484-4. doi:10.1109/ICIT.2005.1600609. - https://doi.org/10.1109/ICIT.2005.1600609
Wolfert, S., Ge, L., Verdouw, C., and Bogaardt, M.-J., 2017. Big Data in Smart Farming-A review. Agricultural Systems, 153:69-80. ISSN 0308-521X. doi:https://doi.org/10.1016/j.agsy.2017.01.023. - https://doi.org/10.1016/j.agsy.2017.01.023
Zhang, H., Chen, H., Guo, Y., Wang, J., Li, G., and Shen, L., 2019. Sensor fault detection and diagnosis for a water source heat pump air-conditioning system based on PCA and preprocessed by combined clustering. Applied Thermal Engineering, 160:114098. ISSN 1359-4311. doi:https://doi.org/10.1016/j.applthermaleng. 2019.114098. - https://doi.org/10.1016/j.applthermaleng.2019.114098
Zheng, J., Wang, H., Song, Z., and Ge, Z., 2019. Ensemble semi-supervised Fisher discriminant analysis model for fault classification in industrial processes. ISA Transactions, 92:109-117. ISSN 0019-0578. doi:https://doi.org/10.1016/j.isatra.2019.02.021. - https://doi.org/10.1016/j.isatra.2019.02.021
Mateos García, N. (2019). Multi-agent system for anomaly detection in Industry 4.0 using Machine Learning techniques. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 8(4), 33–40. https://doi.org/10.14201/ADCAIJ2019843340
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