Abstract
Machine Learning (ML) based data analytics provide methods to cope with the massive data amounts, generated by the various manufacturing processes. In this respect, the maintenance is among the most critical activities concerned by the industrial data analytics in the contexts of industry 4.0. We believe that the maintenance activities can be managed by the predictive processes dwelling on ML methods with the help of meta-learning based data analytics solutions. The challenge is then to facilitate the industry 4.0 actors, who are supposedly not AI specialists, with the application of machine learning. The automated machine learning seems to be the area dealing with this challenge. In this paper, we first show the problematic of assisting industry 4.0 actors to implement ML algorithms in the context of predictive maintenance. We then present a novel AutoML based framework. It aims to enable industry 4.0 actors and researchers, who presumably have limited competencies in machine learning, to generate ML-based data analytics solutions and their deployment in the manufacturing workflows. The framework implements primarily the approaches based on the meta-learning for this purpose. In the context of Industry 4.0 such approaches lead to the implementation of the smart factory concepts. It makes the factory processes more proactive on the basis of predictive knowledge extracted from the various manufacturing devices, sensors, and business processes.
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Usuga Cadavid, J.P., et al.: Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0. J. Intell. Manuf. 31(6), 1531–1558 (2020). https://doi.org/10.1007/s10845-019-01531-7
Wolf, H., et al.: Bringing advanced analytics to manufacturing: a systematic mapping. In: Ameri, F., Stecke, K.E., von Cieminski, G., Kiritsis, D. (eds.) APMS 2019. IAICT, vol. 566, pp. 333–340. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30000-5_42
Hutter, F., Kotthoff, L., Vanschoren, J. (eds.): Automated Machine Learning. TSSCML, Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05318-5
Mustafa, A., Rahimi Azghadi, M.: Automated machine learning for healthcare and clinical notes analysis. Computers 10(2), 24 (2021). https://doi.org/10.3390/computers10020024
Garouani, M., et al.: AMLBID: an auto-explained automated machine learning tool for big industrial data. SoftwareX 17, 100919 (2022). https://doi.org/10.1016/j.softx.2021.100919
Garouani, M., et al.: Towards big industrial data mining through explainable automated machine learning. Int. J. Adv. Manuf. Technol. 120, 1169–1188 (2022). https://doi.org/10.1007/s00170-022-08761-9
Thornton, C., et al.: Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, pp. 847–855. Association for Computing Machinery, New York (2013). https://doi.org/10.1145/2487575.2487629
Olson, R.S., Moore, J.H.: TPOT: a tree-based pipeline optimization tool for automating machine learning. In: Hutter, F., Kotthoff, L., Vanschoren, J. (eds.) Automated Machine Learning. TSSCML, pp. 151–160. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05318-5_8
RapidMiner—Best Data Science & Machine Learning Platform. Rapid-Miner. https://rapidminer.com/
Yang, C., et al.: OBOE: collaborative filtering for AutoML model selection. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1173–1183 (2019). https://doi.org/10.1145/3292500.3330909
Guyon, I., et al.: Analysis of the AutoML challenge series 2015–2018. In: Hutter, F., Kotthoff, L., Vanschoren, J. (eds.) Automated Machine Learning. TSSCML, pp. 177–219. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05318-5_10
Feurer, M., et al.: Auto-sklearn: efficient and robust automated machine learning. In: Hutter, F., Kotthoff, L., Vanschoren, J. (eds.) Automated Machine Learning. TSSCML, pp. 113–134. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05318-5_6
Drori, I., et al.: AlphaD3M machine learning pipeline synthesis (2018)
Luo, G.: PredicT-ML: a tool for automating machine learning model building with big clinical data. Health Inf. Sci. Syst. 4(1), 5 (2016). https://doi.org/10.1186/s13755-016-0018-1
Katz, G., Shin, E.C., Song, D.: ExploreKit: automatic feature generation and selection. In: 2016 IEEE 16th International Conference on Data Mining (ICDM) (2016). https://doi.org/10.1109/ICDM.2016.0123
Nargesian, F., et al.: Learning feature engineering for classification, pp. 2529–2535 (2017)
Garouani, M., et al.: Towards the automation of industrial data science: a meta-learning based approach. In: 23rd International Conference on Enterprise Information Systems, pp. 709–716 (2021). https://doi.org/10.5220/0010457107090716
Reif, M., et al.: Automatic classifier selection for non-experts. Pattern Anal. Appl. 17(1), 83–96 (2012). https://doi.org/10.1007/s10044-012-0280-z
Wang, Q., et al.: ATMSeer: increasing transparency and controllability in automated machine learning. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, pp. 1–12. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3290605.3300911
H2O.Ai—AI Cloud Platform. H2O.ai. https://www.h2o.ai/
AutoML Tables. Google Cloud. https://cloud.google.com/automl-tables/docs
Muñoz, M.A., et al.: Algorithm selection for black-box continuous optimization problems: a survey on methods and challenges. Inf. Sci. 317, 224–245 (2015). https://doi.org/10.1016/j.ins.2015.05.010
Bilalli, B.: Learning the Impact of Data Pre-processing in Data Analysis. Universitat Politècnica de Catalunya, Barcelona (2018)
Samek, W., Müller, K.-R.: Towards explainable artificial intelligence. In: Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., Müller, K.-R. (eds.) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. LNCS (LNAI), vol. 11700, pp. 5–22. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28954-6_1
Villanueva Zacarias, A.G., Reimann, P., Mitschang, B.: A framework to guide the selection and configuration of machine-learning-based data analytics solutions in manufacturing. In: Procedia CIRP, 51st CIRP Conference on Manufacturing Systems, vol. 72, pp. 153–158 (2018). https://doi.org/10.1016/j.procir.2018.03.215
Lechevalier, D., et al.: A methodology for the semi-automatic generation of analytical models in manufacturing. Comput. Ind. 95, 54–67 (2018). https://doi.org/10.1016/j.compind.2017.12.005
Mazumder, R.K., Salman, A.M., Li, Y.: Failure risk analysis of pipelines using data-driven machine learning algorithms. Struct. Saf. 89, 102047 (2021). https://doi.org/10.1016/j.strusafe.2020.102047
Benkedjouh, T., Medjaher, K., Zerhouni, N., Rechak, S.: Health assessment and life prediction of cutting tools based on support vector regression. J. Intell. Manuf. 26(2), 213–223 (2013). https://doi.org/10.1007/s10845-013-0774-6
Costa, C.F., Nascimento, M.A.: IDA 2016 industrial challenge: using machine learning for predicting failures. In: Boström, H., Knobbe, A., Soares, C., Papapetrou, P. (eds.) IDA 2016. LNCS, vol. 9897, pp. 381–386. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46349-0_33
Saravanamurugan, S., et al.: Chatter prediction in boring process using machine learning technique. Int. J. Manuf. Res. (2017). https://doi.org/10.1504/IJMR.2017.10007082
Garouani, M., Ahmad, A., Bouneffa, M., Hamlich, M., Bourguin, G., Lewandowski, A.: Towards meta-learning based data analytics to better assist the domain experts in industry 4.0. In: Dang, N.H.T., Zhang, Y.D., Tavares, J.M.R.S., Chen, B.H. (eds.) ICABDE 2021. LNDECT, vol. 124, pp. 265–277. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-97610-1_22
Garouani, M., et al.: Using meta-learning for automated algorithms selection and configuration: an experimental framework for industrial big data. J. Big Data 9, 57 (2022). https://doi.org/10.1186/s40537-022-00612-4
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This work has been supported, in part, by Hestim, CNRST Morocco, and University of the Littoral Cote d’Opale, Calais France.
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Garouani, M., Hamlich, M., Ahmad, A., Bouneffa, M., Bourguin, G., Lewandowski, A. (2022). Toward an Automatic Assistance Framework for the Selection and Configuration of Machine Learning Based Data Analytics Solutions in Industry 4.0. In: Lazaar, M., Duvallet, C., Touhafi, A., Al Achhab, M. (eds) Proceedings of the 5th International Conference on Big Data and Internet of Things. BDIoT 2021. Lecture Notes in Networks and Systems, vol 489. Springer, Cham. https://doi.org/10.1007/978-3-031-07969-6_1
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