Advanced search
Publication Cover
Production & Manufacturing Research
An Open Access Journal
Volume 11, 2023 - Issue 1
Submit an article Journal homepage
2,090
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Improved GRU prediction of paper pulp press variables using different pre-processing methods

Balduíno César Mateusa EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Lisboa, Portugal;b CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Covilhã, PortugalCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-1953-7268
ORCID Icon,
Mateus Mendesc Instituto Superior de Engenharia de Coimbra, Coimbra, Portugal;d Institute of Systems and Robotics, University of Coimbra, Coimbra, PortugalORCID Iconhttps://orcid.org/0000-0003-4313-7966
ORCID Icon,
José Torres Farinhac Instituto Superior de Engenharia de Coimbra, Coimbra, Portugal;e Centre for Mechanical Engineering, Materials and Processes—CEMMPRE, University of Coimbra, Coimbra, PortugalORCID Iconhttps://orcid.org/0000-0002-9694-8079
ORCID Icon,
António Marques Cardosob CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Covilhã, PortugalORCID Iconhttps://orcid.org/0000-0001-8737-6999
ORCID Icon,
Rui Assisa EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Lisboa, PortugalORCID Iconhttps://orcid.org/0000-0002-1697-055X
ORCID Icon &
Hamzeh Soltanalif Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, IranORCID Iconhttps://orcid.org/0000-0002-1972-1030
ORCID Icon
Article: 2155263 | Received 29 Jul 2022, Accepted 30 Nov 2022, Published online: 23 Dec 2022

References

  • Abbasi, T., Lim, K. H., & Yam, K. S. (2019). Predictive maintenance of oil and gas equipment using recurrent neural network. IOP Conference Series: Materials Science and Engineering, 495:12067.
  • Almeida Pais, J. E. D., Raposo, H. D. N., Farinha, J. T., Cardoso, A. J. M., & Marques, P. A. (2021). Optimizing the life cycle of physical assets through an integrated life cycle assessment method. Energies, 14(19), 6128. https://doi.org/10.3390/en14196128
  • Asgarpour, M., Sørensen, J., Vazquez, S. L., Kulkarni, C. S., Strom, T. H., Hill, B. L., Smalling, K. M., & Quach, C. C. (2018). Bayesian based prognostic model for predictive maintenance of offshore wind farms. International Journal of Prognostics and Health Management, 9(1). https://doi.org/10.36001/ijphm.2018.v9i1.2700
  • Balevi, E., Rabee, F. T. A., & Gitlin, R. D. (2018). Aloha-noma for massive machine-to-machine iot communication. In 2018 IEEE International Conference on Communications (ICC), 1–22.
  • Bury, T. M., Sujith, R., Pavithran, I., Scheffer, M., Lenton, T. M., Anand, M., & Bauch, C. T. (2021). Deep learning for early warning signals of tipping points. Proceedings of the National Academy of Sciences, 118(39):e2106140118.
  • Canizo, M., Onieva, E., Conde, A., Charramendieta, S., & Trujillo, S. (2017). Real-time predictive maintenance for wind turbines using big data frameworks. 2017 IEEE International Conference on Prognostics and Health Management, ICPHM 2017, 70–77.
  • Choi, Y., Kwun, H., Kim, D., Lee, E., & Bae, H. (2020). Method of predictive maintenance for induction furnace based on neural network. 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), 609–612.
  • Chong, M. M., Abraham, A., & Paprzycki, M. (2004). Traffic accident analysis using decision trees and neural networks. International Journal of Information Technology and Computer Science, 6, 22–28. https://doi.org/10.48550/arXiv.cs/0405050
  • Cho, K., van Merrienboer, B., Bahdanau, D., & Bengio, Y. 2014. On the properties of neural machine translation: Encoder-decoder approaches. CoRR, 1409, abs, 1259. https://doi.org/10.48550/arXiv.1409.1259
  • Chui, K. T., Gupta, B. B., & Vasant, P. (2021). A genetic algorithm optimized rnn-lstm model for remaining useful life prediction of turbofan engine. Electronics, 10(3), 285. https://doi.org/10.3390/electronics10030285
  • Cleveland, W. S. (1981). Lowess: A program for smoothing scatterplots by robust locally weighted regression. The American Statistician, 35(1), 54. https://doi.org/10.2307/2683591
  • Cline, B., Niculescu, R. S., Huffman, D., & Deckel, B. (2017). Predictive maintenance applications for machine learning. Proceedings - Annual Reliability and Maintainability Symposium.
  • Couso, I., Borgelt, C., Hullermeier, E., & Kruse, R. (2019). Fuzzy sets in data analysis: From statistical foundations to machine learning. IEEE Computational Intelligence Magazine, 14(1), 31–44. https://doi.org/10.1109/MCI.2018.2881642
  • Cross, M. (1988). Raising the value of maintenance in the corporate environment. Management Research News, 11(3), 8–11. https://doi.org/10.1108/eb027976
  • Dai, Y., Wang, Y., Leng, M., Yang, X., & Zhou, Q. (2022). Lowess smoothing and random forest based gru model: A short-term photovoltaic power generation forecasting method. Energy, 256, 124661. https://doi.org/10.1016/j.energy.2022.124661
  • Dalzochio, J., Kunst, R., Pignaton, E., Binotto, A., Sanyal, S., Favilla, J., & Barbosa, J. (2020). Machine learning and reasoning for predictive maintenance in industry 4.0: Current status and challenges. Computers in Industry, 123, 103298. https://doi.org/10.1016/j.compind.2020.103298
  • Drath, R., & Horch, A. (2014). Industrie 4.0: Hit or hype? [industry forum]. IEEE Industrial Electronics Magazine, 8(2), 56–58. https://doi.org/10.1109/MIE.2014.2312079
  • Edwards, D. J., Holt, G. D., & Harris, F. (1998). Predictive maintenance techniques and their relevance to construction plant. Journal of Quality in Maintenance Engineering, 4(1), 25–37. https://doi.org/10.1108/13552519810369057
  • Eklund, P. (1998). of Intelligent Information Systems. v9 i1, A. H., and undefined 2002. A performance survey of public domain supervised machine learning algorithms. https://www.researchgate.net/profile/Peter-Eklund/publication/2767745_A_Performance_Survey_of_Public_Domain_Supervised_Machine_Learning_Algorithms/links/0fcfd50b315bee877a000000/A-Performance-Survey-of-Public-Domain-Supervised-Machine-Learning-Algorithms.pdf
  • Erboz, G. (2017). How to define industry 4.0: Main pillars of industry 4.0. Managerial Trends in the Development of Enterprises in Globalization Era, 761, 767. https://www.researchgate.net/profile/Gizem-Erboz-2/publication/326557388_How_To_Define_Industry_40_Main_Pillars_Of_Industry_40/links/5fc553374585152e9be7f201/How-To-Define-Industry-40-Main-Pillars-Of-Industry-40.pdf
  • Florea, G., Paraschiv, A., & Cimpoesu, E. (2012). Wind farm noise monitoring used for predictive maintenance. IFAC Proceedings Volumes, 45:1822–1827.
  • Gao, S., Huang, Y., Zhang, S., Han, J., Wang, G., Zhang, M., & Lin, Q. (2020). Short-term runoff prediction with gru and lstm networks without requiring time step optimization during sample generation. Journal of Hydrology, 589, 125188. https://doi.org/10.1016/j.jhydrol.2020.125188
  • Glistau, E., & Coello Machado, N. I. (2018). Industry 4.0, logistics 4.0 and materials - chances and solutions. Materials Science Forum, 919, 307–314. https://doi.org/10.4028/www.scientific.net/MSF.919.307
  • Gorski, E. G., de Freitas Rocha Loures, E., Santos, E. A. P., Kondo, R. E., & Martins, G. R. D. N. (2021). Towards a smart workflow in cmms/eam systems: An approach based on ml and mcdm. Journal of Industrial Information Integration, 100278. https://doi.org/10.1016/j.jii.2021.100278
  • He, K., Liu, Z., Sun, Y., Mao, L., & Lu, S. (2022). Degradation prediction of proton exchange membrane fuel cell using auto-encoder based health indicator and long short-term memory network. International Journal of Hydrogen Energy, 47, 35055–35067. https://doi.org/10.1016/j.ijhydene.2022.08.092
  • Huang, Y., & Li, G. (2010). Descriptive models for internet of things. In 2010 International Conference on Intelligent Control and Information Processing, 483–486.
  • Husain, S., Prasad, A., Kunz, A., Papageorgiou, A., & Song, J. (2014). Recent trends in standards related to the internet of things and machine-to-machine communications. Journal of Information and Communication Convergence Engineering, 12(4), 228–236. https://doi.org/10.6109/jicce.2014.12.4.228
  • Jabeur, S. B., Gharib, C., Mefteh-Wali, S., & Arfi, W. B. (2021). Catboost model and artificial intelligence techniques for corporate failure prediction. Technological Forecasting and Social Change, 166, 120658. https://doi.org/10.1016/j.techfore.2021.120658
  • Jeenanunta, C., Abeyrathna, K. D., Dilhani, M. H. M. R. S., Hnin, S. W., & Phyo, P. P. (2019). Time series outlier detection for short-term electricity load demand forecasting. International Scientific Journal of Engineering and Technology (ISJET), 2(1), 37–50. https://ph02.tci-thaijo.org/index.php/isjet/article/view/175908
  • Jezzini, A., Ayache, M., Elkhansa, L., Makki, B., & Zein, M. (2013). Effects of predictive maintenance(pdm), proactive maintenace(pom) & preventive maintenance(pm) on minimizing the faults in medical instruments. In 2013 2nd International Conference on Advances in Biomedical Engineering, 53–56.
  • Ji, W., & Wang, L. (2017). Big data analytics based fault prediction for shop floor scheduling. Journal of Manufacturing Systems, 43, 187–194. https://doi.org/10.1016/j.jmsy.2017.03.008
  • Kalsoom, T., Ramzan, N., Ahmed, S., & Ur-Rehman, M. (2020). Advances in sensor technologies in the era of smart factory and industry 4.0. Sensors, 20(23), 6783. https://doi.org/10.3390/s20236783
  • Kim, D. Y., Jeong, Y. S., & Kim, S. 2017. Data-filtering system to avoid total data distortion in iot networking. Symmetry, 9(16), 16. 2017. https://doi.org/10.3390/sym9010016.
  • Koprinkova-Hristova, P. D., Hadjiski, M. B., Doukovska, L. A., & Beloreshki, S. V. (2011). Recurrent neural networks for predictive maintenance of mill fan systems. International Journal of Electronics and Telecommunications, 57(3), 401–406. https://doi.org/10.2478/v10177-011-0055-2
  • Kulkarni, H., Thangam, M., & Amin, A. P. (2021). Artificial neural network-based prediction of prolonged length of stay and need for post-acute care in acute coronary syndrome patients undergoing percutaneous coronary intervention. European Journal of Clinical Investigation, 51(3), e13406. https://doi.org/10.1111/eci.13406
  • Lee, J., Ni, J., Djurdjanovic, D., Qiu, H., & Liao, H. 2006. Intelligent prognostics tools and e-maintenance. Computers in Industry, 57(6), 476–489. E-maintenance Special Issue. https://doi.org/10.1016/j.compind.2006.02.014
  • Lei, X., Sandborn, P., Bakhshi, R., Kashani-Pour, A., & Goudarzi, N. (2015). Phm based predictive maintenance optimization for offshore wind farms. In 2015 IEEE Conference on Prognostics and Health Management (PHM), 1–8.
  • L’Heureux, A., Grolinger, K., Elyamany, H. F., & Capretz, M. A. (2017). Machine learning with big data: Challenges and approaches. IEEE Access, 5, 7776–7797. https://doi.org/10.1109/ACCESS.2017.2696365
  • Lim, T. S., Loh, W. Y., & Shih, Y. S. 2000. A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning, 40(3), 203–228. 2000. https://doi.org/10.1023/A:1007608224229
  • Li, H., Parikh, D., He, Q., Qian, B., Li, Z., Fang, D., & Hampapur, A. (2014). Improving rail network velocity: A machine learning approach to predictive maintenance.Transportation Research Part C: Emerging Technologies, 45, 17–26. Advances in Computing and Communications and their Impact on Transportation Science and Technologies. https://doi.org/10.1016/j.trc.2014.04.013
  • Liu, R., Yang, B., Zio, E., & Chen, X. (2018). Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108, 33–47. https://doi.org/10.1016/j.ymssp.2018.02.016
  • Li, C., Xie, C., Zhang, B., Chen, C., & Han, J. (2018). Deep fisher discriminant learning for mobile hand gesture recognition. Pattern Recognition, 77, 276–288. https://doi.org/10.1016/j.patcog.2017.12.023
  • Lv, Y., Zhou, Q., Li, Y., & Li, W. (2021). A predictive maintenance system for multi-granularity faults based on adabelief-bp neural network and fuzzy decision making. Advanced Engineering Informatics, 49, 101318. https://doi.org/10.1016/j.aei.2021.101318
  • Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., & Loumos, V. (2009). Dropout prediction in e-learning courses through the combination of machine learning techniques. Computers & Education, 53(3), 950–965. https://doi.org/10.1016/j.compedu.2009.05.010
  • Markiewicz, M., Wielgosz, M., Bochenski, M., Tabaczynski, W., Konieczny, T., & Kowalczyk, L. (2019). Predictive maintenance of induction motors using ultra-low power wireless sensors and compressed recurrent neural networks. IEEE Access, 7, 178891–178902. https://doi.org/10.1109/ACCESS.2019.2953019
  • Martins, A. B., Farinha, J. T., & Cardoso, A. M. (2020). Calibration and certification of industrial sensors – A global review. WSEAS Transactions on Systems and Control, 15, 394–416. https://doi.org/10.37394/23203.2020.15.41
  • Martins, A., Fonseca, I., Farinha, J. T., Reis, J., & Cardoso, A. M. 2021. Maintenance prediction through sensing using hidden Markov models—a case study. Applied Sciences, 11(7685), 7685. 2021. https://doi.org/10.3390/app11167685
  • Mateus, B. C., Mendes, M., Farinha, J. T., Assis, R., & Cardoso, A. M. (2021). Comparing lstm and gru models to predict the condition of a pulp paper press. Energies, 14(21), 6958. https://doi.org/10.3390/en14216958
  • Miller, K., Hettinger, C., Humpherys, J., Jarvis, T., & Kartchner, D. (2017). Forward thinking: Building deep random forests.
  • Narendra, N., Ponnalagu, K., Ghose, A., & Tamilselvam, S. (2015). Goal-driven context-aware data filtering in iot-based systems. 2015 IEEE 18th International Conference on Intelligent Transportation Systems, 2172–2179.
  • Nascimento, R. G., & Viana, F. A. (2019). Fleet prognosis with physics-informed recurrent neural networks. Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring, 2:1740–1747.
  • Phyo, P. P., Jeenanunta, C., & Hashimoto, K. (2019). Electricity load forecasting in Thailand using deep learning models. International Journal of Electrical and Electronic Engineering & Telecommunications, 8(4), 221–225. https://doi.org/10.18178/ijeetc.8.4.221-225
  • Qiu, J., Wu, Q., Ding, G., Xu, Y., & Feng, S. (2016). A survey of machine learning for big data processing. Eurasip Journal on Advances in Signal Processing, 2016, 1–16. https://doi.org/10.1186/s13634-016-0355-x
  • Rivas, A., Fraile, J. M., Chamoso, P., González-Briones, A., Sittón, I., & Corchado, J. M. (2019). A predictive maintenance model using recurrent neural networks. Advances in Intelligent Systems and Computing, 950, 261–270. https://doi.org/10.1007/978-3-030-20055-8_25
  • Rodrigues, J. A., Farinha, J. T., Mendes, M., Mateus, R., & Cardoso, A. (2021). Short and long forecast to implement predictive maintenance in a pulp industry. Eksploatacja I Niezawodnosc - Maintenance and Reliability, 24(1), 33–41. https://doi.org/10.17531/ein.2022.1.5
  • Santra, A. S., & Lin, J.-L. 2019,January. Integrating long short-term memory and genetic algorithm for short-term load forecasting. Energies 12:11 2040. Number: 11 Publisher: Multidisciplinary Digital Publishing Institute https://doi.org/10.3390/en12112040
  • Schwenk, H., & Bengio, Y. (2000). Boosting neural networks. Neural Computation, 12(8), 1869–1887. https://doi.org/10.1162/089976600300015178
  • Sherif, Y. S., & Smith, M. L. (1981). Optimal maintenance models for systems subject to failure–a review. Naval Research Logistics Quarterly, 28, 47–74. https://doi.org/10.1002/nav.3800280104
  • Spendla, L., Kebisek, M., Tanuska, P., & Hrcka, L. (2017). Concept of predictive maintenance of production systems in accordance with industry 4.0. In 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), 000405–000410.
  • Tipping, M. E. (2003). Bayesian inference: An introduction to principles and practice in machine learning. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3176, 41–62. https://doi.org/10.1007/978-3-540-28650-9_3
  • Tsibulnikova, M. R., Pham, V. A., Aikina, T. Y., Xue-feng, L., Xiao-ben, L., Jan-ding, H., Al, Abbasi, T., Lim, K. H., & Yam, K. S. (2019). Predictive maintenance of oil and gas equipment using recurrent neural network. IOP Conference Series: Materials Science and Engineering, 495:12067.
  • Turnbull, A., & Carroll, J. (2021). Cost benefit of implementing advanced monitoring and predictive maintenance strategies for offshore wind farms. Energies, 14(16), 4922. https://doi.org/10.3390/en14164922
  • Udo, W., & Muhammad, Y. (2021). Data-driven predictive maintenance of wind turbine based on scada data. IEEE Access, 9, 162370–162388. https://doi.org/10.1109/ACCESS.2021.3132684
  • Wang, S.-C. (2003). Artificial neural network. Interdisciplinary Computing in Java Programming, 81–100. https://doi.org/10.1007/978-1-4615-0377-4_5
  • Wang, F.-K., Amogne, Z. E., Chou, J.-H., & Tseng, C. (2022). Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism. Energy, 254, 124344. https://doi.org/10.1016/j.energy.2022.124344
  • Wang, Q., Bu, S., & He, Z. (2020). Achieving predictive and proactive maintenance for high-speed railway power equipment with lstm-rnn. IEEE Transactions on Industrial Informatics, 16(10), 6509–6517. https://doi.org/10.1109/TII.2020.2966033
  • Wu, H., Huang, A., & Sutherland, J. W. (2020). Avoiding environmental consequences of equipment failure via an lstm-based model for predictive maintenance. Procedia Manufacturing, 43:666–673. Sustainable Manufacturing - Hand in Hand to Sustainability on Globe: Proceedings of the 17th Global Conference on Sustainable Manufacturing.
  • Yam, R. C., Tse, P. W., Li, L., & Tu, P. 2001. Intelligent predictive decision support system for condition-based maintenance. The International Journal of Advanced Manufacturing Technology, 17(5), 383–391. 2001. https://doi.org/10.1007/s001700170173
  • Zfle, M., Moog, F., Lesch, V., Krupitzer, C., & Kounev, S. (2021). A machine learning-based workflow for automatic detection of anomalies in machine tools. ISA Transactions, 121, 180–190. https://doi.org/10.1016/j.isatra.2021.03.036
  • Zhang, J., Zeng, Y., & Starly, B. (2021). Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis. SN Applied Sciences, 3(4), 1–13. https://doi.org/10.1007/s42452-021-04427-5
  • Zhikun, H., Bin, J., Linzi, Y., & Xiaolong, C. (2013). Predictive maintenance strategy of variable period of power transformer based on reliability and cost. 2013 25th Chinese Control and Decision Conference, CCDC 2013, 4803–4807.
  • Zhou, C., Liu, X., Chen, W., Xu, F., & Cao, B. (2018). Optimal sliding mode control for an active suspension system based on a genetic algorithm. Algorithms, 11(12), 205. https://doi.org/10.3390/a11120205
  • Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350–361. https://doi.org/10.1016/j.neucom.2017.01.026
  • Zibar, D., Piels, M., Jones, R., & Schäeffer, C. G. (2016). Machine learning techniques in optical communication. Journal of Lightwave Technology, 34(6), 1442–1452. https://doi.org/10.1109/JLT.2015.2508502

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.