Abstract
Predictive maintenance is fundamental for modern industries, in order to improve the physical assets availability, decision making and rationalize costs. That requires deployment of sensor networks, data storage and development of data treatment methods that can satisfy the quality required in the forecasting models. The present paper describes a case study where data collected in an industrial pulp paper press was pre-processed and used to predict future behavior, aiming to anticipate potential failures, optimize predictive maintenance and physical assets availability. The data were processed and analyzed, outliers identified and treated. Time series models were used to predict short-term future behavior. The results show that it is possible to predict future values up to ten days in advance with good accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pais, E., Farinha, J.T., Cardoso, A.J.M., Raposo, H.: Optimizing the life cycle of physical assets—a review. WSEAS Trans. Syst. Control 15, 417–430 (2020). https://doi.org/10.37394/23203.2020.15.42
Chaudhuri, S., Dayal, U.: An overview of data warehousing and OLAP technology. ACM SIGMOD Rec. 26(1), 65–74 (1997). https://doi.org/10.1145/248603.248616
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The KDD process for extracting useful knowledge from volumes of data. Commun. ACM 39(11), 27–34 (1996). https://doi.org/10.1145/240455.240464
Prado, L.O., Ribeiro, P.F., Duque, C.A., Abdel Aleem, S.H.E.: Chapter 19—Modeling and processing of smart grids big data: study case of a university research building. In: Abdel Aleem, S.H.E., Abdelaziz, A.Y., Zobaa, A.F., Bansal, R. (Eds.) Decision Making Applications in Modern Power Systems, pp. 507–538. Academic Press (2020)
Martins, A.B., Torres Farinha, J., Marques Cardoso, A.: Calibration and certification of industrial sensors—a global review. WSEAS Trans. Syst. Control 15, 394–416 (2020). https://doi.org/10.37394/23203.2020.15.41
Gong, Z., Wang, W., Ku, W.-S.: Adversarial and clean data are not twins. Ar**v170404960 Cs, Apr. 2017, Accessed: Mar. 02, 2021. [Online]. Available: http://arxiv.org/abs/1704.04960
Veit, A., Alldrin, N., Chechik, G., Krasin, I., Gupta, A., Belongie, S.: Learning From Noisy Large-Scale Datasets With Minimal Supervision, pp. 839–847, Accessed: Mar. 02, 2021. [Online] (2017). Available: https://openaccess.thecvf.com/content_cvpr_2017/html/Veit_Learning_From_Noisy_CVPR_2017_paper.html
Plutowski, M., White, H.: Selecting concise training sets from clean data. IEEE Trans. Neural Netw. 4(2), 305–318 (1993). https://doi.org/10.1109/72.207618
Zhang, Z.: Neural networks: further insights into error function, generalized weights and others. Ann. Transl. Med. 4(16) (2016). https://doi.org/10.21037/atm.2016.05.37
Siami-Namini, S., Namin, A.S.: Forecasting economics and financial time series: ARIMA vs. LSTM, Ar**v180306386 Cs Q-Fin Stat, Mar. 2018, Accessed: Mar. 09, 2021. [Online]. Available: http://arxiv.org/abs/1803.06386
Mateus, B., Farinha, J.T., Cardoso, A.M.: Production Optimization Versus Asset Availability—A Review, vol. 15, p. 13 (2020). https://doi.org/10.37394/23203.2020.15.33
Kaastra, I., Boyd, M.: Designing a neural network for forecasting financial and economic time series. Neurocomputing 10(3), 215–236 (1996). https://doi.org/10.1016/0925-2312(95)00039-9
Hecht-Nielsen, R.: Neurocomputer applications. In: Neural Computers, pp. 445–453, Berlin. https://doi.org/10.1007/978-3-642-83740-1_45
Jimenez, V.J., Bouhmala, N., Gausdal, A.H.: Develo** a predictive maintenance model for vessel machinery. J. Ocean Eng. Sci. 5(4), 358–386 (2020). https://doi.org/10.1016/j.joes.2020.03.003
Rodrigues, J., Cost, I., Farinha, J.T., Mendes, M., Margalho, L.: Predicting motor oil condition using artificial neural networks and principal component analysis. Eksploat. Niezawodn. - Maint. Reliab. 22(3), 440–448 (2020). https://doi.org/10.17531/ein.2020.3.6
Daniyan, I., Mpofu, K., Oyesola, M., Ramatsetse, B., Adeodu, A.: Artificial intelligence for predictive maintenance in the railcar learning factories. Proc. Manuf. 45, 13–18 (2020). https://doi.org/10.1016/j.promfg.2020.04.032
Ayvaz, S., Alpay, K.: Predictive maintenance system for production lines in manufacturing: a machine learning approach using IoT data in real-time. Expert Syst. Appl. 173, 114598 (2021). https://doi.org/10.1016/j.eswa.2021.114598
Huang, X., Zanni-Merk, C., Crémilleux, B.: Enhancing Deep Learning with semantics: an application to manufacturing time series analysis. Proc. Comput. Sci. 159, 437–446 (2019). https://doi.org/10.1016/j.procs.2019.09.198
Nti, I.K., Adekoya, A.F., Weyori, B.A.: A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction. J. Big Data 8(1) (2021). https://doi.org/10.1186/s40537-020-00400-y
Liu, M.-D., Ding, L., Bai, Y.-L.: Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction. Energy Convers. Manag. 233, 113917 (2021). https://doi.org/10.1016/j.enconman.2021.113917
Aydin, O., Guldamlasioglu, S: Using LSTM Networks to Predict Engine Condition on Large Scale Data Processing Framework, pp. 281–285 (2017). https://doi.org/10.1109/ICEEE2.2017.7935834
Khandelwal, I., Adhikari, R., Verma, G.: Time series forecasting using hybrid ARIMA and ANN models based on DWT decomposition. Proc. Comput. Sci. 48, 173–179 (2015). https://doi.org/10.1016/j.procs.2015.04.167
Yip, H., Fan, H., Chiang, Y.: Predicting the maintenance cost of construction equipment: comparison between general regression neural network and Box-Jenkins time series models. Autom. Constr. 38, 30–38 (2014). https://doi.org/10.1016/j.autcon.2013.10.024
Gui, Z., et al.: LSI-LSTM: an attention-aware LSTM for real-time driving destination prediction by considering location semantics and location importance of trajectory points. Neurocomputing 440, 72–88 (2021). https://doi.org/10.1016/j.neucom.2021.01.067
Acknowledgements
The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowvska-Curie grant agreement 871284 project SSHARE and the European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under Project POCI-01-0145-FEDER-029494, and by National Funds through the FCT—Portuguese Foundation for Science and Technology, under Projects PTDC/EEI-EEE/29494/2017, UIDB/04131/2020, and UIDP/04131/2020.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mateus, B., Mendes, M., Farinha, J.T., Martins, A.B., Cardoso, A.M. (2023). Data Analysis for Predictive Maintenance Using Time Series and Deep Learning Models—A Case Study in a Pulp Paper Industry. In: Zhang, H., Feng, G., Wang, H., Gu, F., Sinha, J.K. (eds) Proceedings of IncoME-VI and TEPEN 2021. Mechanisms and Machine Science, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-030-99075-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-99075-6_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-99074-9
Online ISBN: 978-3-030-99075-6
eBook Packages: EngineeringEngineering (R0)