Modeling System Based on Machine Learning Approaches for Predictive Maintenance Applications


Industry 4.0 must respond to some challenges such as the flexibility and robustness of unexpected conditions, as well as the degree of system autonomy, something that is still lacking. The evolution of Industry 4.0 aims at converting purely mechanical machines into machines with self-learning capacity in order to improve overall performance  and contribute to the optimization of maintenance. An important contribution of Industry 4.0 in the industrial sector is predictive maintenance and prescriptive maintenance. This article should be analysed as a methodology proposal to implement an automatic forecasting model in a test bench for the recognition of a machine’s failure and contribute to the development of algorithms for preventive and descriptive maintenance.

Keywords: Industry 4.0, Artificial intelligence, Machine learning, Predictive maintenance, Prescriptive maintenance

[1] Fernandes, A. C., Genesis and current dynamics of the ”Industry 4.0” concept: a bibliometric approach”, (Doctoral dissertation), 2018.

[2] Lee, J. Y., Kang, H. S., Do Noh, S., “MAS2: an integrated modeling and simulation-based life cycle evaluation approach for sustainable manufacturing”. Journal of Cleaner production, 66, 146-163, 2014.

[3] Bahrin, M. A. K., Othman, M. F., Azli, N. N., Talib, M. F., “Industry 4.0: A review on industrial automation and robotic”. Jurnal Teknologi, 78(6-13), 137-143, 2016.

[4] Wang, S., Wan, J., Li, D., Zhang, C., “Implementing smart factory of industrie 4.0: an outlook”. International Journal of Distributed Sensor Networks, 12(1), 3159805. (2016).

[5] Valdeza, A. C., Braunera, P., Schaara, A. K., Holzingerb, A., Zieflea, M., “Reducing complexity with simplicity-usability methods for industry 4.0”. In Proceedings 19th triennial congress of the IEA (Vol. 9, p. 14), August 2015.

[6] Baena, F., Guarin, A., Mora, J., Sauza, J., Retat, S., “Learning factory: The path to industry 4.0”. Procedia Manufacturing, 9, 73-80, 2017.

[7] Ibarra, D., Ganzarain, J., Igartua, J. I., “Business model innovation through Industry 4.0: A review”. Procedia Manufacturing, 22, 4-10, 2018.

[8] Vaidya, S., Ambad, P., & Bhosle, S., “Industry 4.0–a glimpse”. Procedia Manufacturing, 20, 233-238, 2018.

[9] Busch, M., de Lange, J., Kelzenberg, C., Schuh, G., “Achieving Process Efficiency and Stability in Serial Production Through an Innovative Service System Based on Predictive Maintenance”. In Congress of the German Academic Association for Production Technology (pp. 657-666). Springer, Cham., November 2018.

[10] Aragon, R. A., “The Industrial Revolution 4.0: transformations in organizations and human management in the period 2015-2019”.

[11] Lee, S. M., Lee, D., Kim, Y. S., “The quality management ecosystem for predictive maintenance in the Industry 4.0 era”. International Journal of Quality Innovation, 5(1), 4, 2019.

[12] Ferreiro, S., Konde, E., Fernández, S., Prado, A., “Industry 4.0: Predictive Intelligent Maintenance for Production Equipment”. In European Conference of the Prognostics and Health Management Society, no (pp. 1-8), June 2016.

[13] Koch, V., Kuge, S., Geissbauer, R., Schrauf, S., “Industry 4.0: Opportunities and challenges of the industrial internet”. Strategy & PwC. 2014.

[14] Bowden, D., Marguglio, A., Morabito, L., Napione, C., Panicucci, S., Nikolakis, N., Makris, S., Coppo, G., Andolina, S., Macii, A., Macii, E., “A Cloud-to-edge Architecture for Predictive Analytics”. InEDBT/ICDT Workshops, 2019.

[15] Adhikari, R., and R. K. Agrawal. ”An introductory study on time series modeling and forecasting, 2013.” arXiv preprint arXiv:1302.6613 (2019).

[16] Rodrigues, F., Cardeira, C., & Calado, J. M. F., “The daily and hourly energy consumption and load forecasting using artificial neural network method: a case study using a set of 93 households in Portugal”. Energy Procedia, 62, 220-229, 2014.

[17] Ulbricht, R., Hahmann, M., Donker, H., Lehner, W., “Systematical evaluation of solar energy supply forecasts”. In International Workshop on Data Analytics for Renewable Energy Integration (pp. 108-121). Springer, Cham, September 2014.

[18] Cavallo, J., Marinescu, A., Dusparic, I., Clarke, S., ”Evaluation of forecasting methods for very small- scale networks.” International Workshop on Data Analytics for Renewable Energy Integration. Springer, Cham, 2015.

[19] Bonetto, R., Michele Rossi. ”Parallel multi-step ahead power demand forecasting through NAR neural networks.” 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2016.

[20] Xu, J., Yue, M., Katramatos, D., Yoo, S., “Spatial-temporal load forecasting using AMI data”. In 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm) (pp. 612-618). IEEE. November 2016.

[21] Hosein, S., Hosein, P. “Load forecasting using deep neural networks”. IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) (pp. 1-5). IEEE, April 2017.

[22] Rodrigues, F., Cardeira, C., Calado, J. M. F., Melício, R., “Load profile analysis tool for electrical appliances in households assisted by CPS. Energy Procedia, 106, 215-224, 2016.

[23] Rodrigues, F., Cardeira, C., Calado, J. M. F., Melício, R., “Family houses energy consumption forecast tools for smart grid management”. In CONTROLO 2016 (pp. 691-699). Springer, Cham, 2017.

[24] Rodrigues, F., Cardeira, C., Calado, J. M. F., “Neural Networks Applied to Short Term Load Forecasting: A Case Study”. In Smart Energy Control Systems for Sustainable Buildings (pp. 173-197). Springer, Cham, 2017.

[25] Lepouze, A., Zufferey, T., Hug, G., “Design of an Automatic Forecasting Engine for Real- time State Estimation in Distribution Grids”. Project Number 1723, April 30, 2018.

[26] Sevlian, R., Rajagopal, R., “Short term electricity load forecasting on varying levels of aggregation. arXiv preprint arXiv:1404.0058, 2014.

[27] Dagnely, P., Ruette, T., Tourwé, T., Tsiporkova, E., Verhelst, C., ”Predicting hourly energy consumption. Can regression modeling improve on an autoregressive baseline?” International Workshop on Data Analytics for Renewable Energy Integration. Springer, Cham, 2015.

[28] Ryu, S., Noh, J., Kim, H., “Deep neural network based demand side short term load forecasting”. Energies, 10(1), 3, 2016.

[29] Zufferey, T., Ulbig, A., Koch, S., Hug, G., “Forecasting of smart meter time series based on neural networks”. In International Workshop on Data Analytics for Renewable Energy Integration (pp. 10-21). Springer, Cham, September 2016.

[30] Cheng, Yao, Xu, C., Mashima, D., Thing, V. L., Wu, Y., ”PowerLSTM: Power demand forecasting using long short-term memory neural network.” International Conference on Advanced Data Mining and Applications. Springer, Cham, 2017.

[31] Mancuso, A.C.B, Werner, L.,”Review of combining forecasts approaches.” Independent journal of management & production 4.1 (2013): 248-277.

[32] Widodo, A., Budi, I., “Combination of time series forecasts using neural network”. In Proceedings of the 2011 International Conference on Electrical Engineering and Informatics (pp. 1-6). IEEE, July 2011.

[33] Cang, S., Yu, H., “A combination selection algorithm on forecasting”. European Journal of Operational Research, 234(1), 127-139, 2014.

[34] Yang, L., Li, B., Li, X., Yin, L., “A novel combination forecasting algorithm based on time series”. International Journal of Database Theory and Application, 8(2), 157-170, 2015.