{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:14:18Z","timestamp":1761808458348,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T00:00:00Z","timestamp":1625011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Predictive maintenance is very important in industrial plants to support decisions aiming to maximize maintenance investments and equipment\u2019s availability. This paper presents predictive models based on long short-term memory neural networks, applied to a dataset of sensor readings. The aim is to forecast future equipment statuses based on data from an industrial paper press. The datasets contain data from a three-year period. Data are pre-processed and the neural networks are optimized to minimize prediction errors. The results show that it is possible to predict future behavior up to one month in advance with reasonable confidence. Based on these results, it is possible to anticipate and optimize maintenance decisions, as well as continue research to improve the reliability of the model.<\/jats:p>","DOI":"10.3390\/app11136101","type":"journal-article","created":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T10:03:19Z","timestamp":1625047399000},"page":"6101","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Anticipating Future Behavior of an Industrial Press Using LSTM Networks"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1953-7268","authenticated-orcid":false,"given":"Baldu\u00edno C\u00e9sar","family":"Mateus","sequence":"first","affiliation":[{"name":"EIGeS\u2014Research Centre in Industrial Engineering, Management and Sustainability, Lus\u00f3fona University, Campo Grande, 376, 1749-024 Lisboa, Portugal"},{"name":"CISE\u2014Electromechatronic Systems Research Centre, University of Beira Interior, Cal\u00e7ada Fonte do Lameiro, P\u201362001-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4313-7966","authenticated-orcid":false,"given":"Mateus","family":"Mendes","sequence":"additional","affiliation":[{"name":"Polytechnic of Coimbra, ISEC, 3045-093 Coimbra, Portugal"},{"name":"Institute of Systems and Robotics, University of Coimbra, 3004-531 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9694-8079","authenticated-orcid":false,"given":"Jos\u00e9 Torres","family":"Farinha","sequence":"additional","affiliation":[{"name":"Polytechnic of Coimbra, ISEC, 3045-093 Coimbra, Portugal"},{"name":"Centre for Mechanical Engineering, Materials and Processes\u2014CEMMPRE, 3030-788 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8737-6999","authenticated-orcid":false,"given":"Ant\u00f3nio Marques","family":"Cardoso","sequence":"additional","affiliation":[{"name":"CISE\u2014Electromechatronic Systems Research Centre, University of Beira Interior, Cal\u00e7ada Fonte do Lameiro, P\u201362001-001 Covilh\u00e3, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.jmsy.2019.11.004","article-title":"Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case","volume":"54","author":"Sahal","year":"2020","journal-title":"J. Manuf. Syst."},{"key":"ref_2","unstructured":"Ahmed, M.S., and Cook, A.R. (2020). Analysis of Freeway Traffic Time-Series Data by Using Box-Jenkins Techniques, Transportation Research Board."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102026","DOI":"10.1016\/j.rcim.2020.102026","article-title":"A big data-driven framework for sustainable and smart additive manufacturing","volume":"67","author":"Majeed","year":"2021","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_4","unstructured":"Ferreiro, S., Konde, E., Fern\u00e1ndez, S., and Prado, A. (2016). Industry 4.0: Predictive intelligent maintenance for production equipment. European Conference of the Prognostics and Health Management Society, Available online: https:\/\/www.semanticscholar.org\/paper\/INDUSTRY-4-.-0-%3A-Predictive-Intelligent-Maintenance-Ferreiro-Konde\/638c2b72a747ea4b82e098572be820083dca9c7a."},{"key":"ref_5","first-page":"259","article-title":"Intelligent predictive maintenance (IPdM) system\u2014Industry 4.0 scenario","volume":"113","author":"Wang","year":"2016","journal-title":"WIT Trans. Eng. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1109\/TII.2019.2915846","article-title":"A global manufacturing big data ecosystem for fault detection in predictive maintenance","volume":"16","author":"Yu","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"417","DOI":"10.37394\/23203.2020.15.42","article-title":"Optimizing the Life Cycle of Physical Assets\u2014A Review","volume":"15","author":"Pais","year":"2020","journal-title":"WSEAS Trans. Syst. Control"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"394","DOI":"10.37394\/23203.2020.15.41","article-title":"Calibration and Certification of Industrial Sensors\u2014A Global Review","volume":"15","author":"Martins","year":"2020","journal-title":"WSEAS Trans. Syst. Control"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"440","DOI":"10.17531\/ein.2020.3.6","article-title":"Predicting motor oil condition using artificial neural networks and principal component analysis","volume":"22","author":"Rodrigues","year":"2020","journal-title":"Eksploat. Niezawodn."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Torres Farinha, J. (2018). Asset Maintenance Engineering Methodologies, CRC Press.","DOI":"10.1201\/9781315232867"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1108\/09576060310477861","article-title":"An approach to design of maintenance float systems","volume":"14","author":"Chen","year":"2003","journal-title":"Integr. Manuf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.promfg.2020.04.032","article-title":"Artificial intelligence for predictive maintenance in the railcar learning factories","volume":"45","author":"Daniyan","year":"2020","journal-title":"Procedia Manuf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1016\/j.ifacol.2018.11.783","article-title":"Two-Stage Artificial Neural Network Model for Short-Term Load Forecasting","volume":"51","author":"Hsu","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"320","DOI":"10.37394\/23203.2020.15.33","article-title":"Production Optimization versus Asset Availability\u2014A Review","volume":"15","year":"2020","journal-title":"WSEAS Trans. Syst. Control"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.joes.2020.03.003","article-title":"Developing a predictive maintenance model for vessel machinery","volume":"5","author":"Jimenez","year":"2020","journal-title":"J. Ocean. Eng. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"114598","DOI":"10.1016\/j.eswa.2021.114598","article-title":"Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time","volume":"173","author":"Ayvaz","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"42","DOI":"10.3389\/fnbot.2017.00042","article-title":"Continuous Timescale Long-Short Term Memory Neural Network for Human Intent Understanding","volume":"11","author":"Yu","year":"2017","journal-title":"Front. Neurorobot."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Aydin, O., and Guldamlasioglu, S. (2017, January 8\u201310). Using LSTM networks to predict engine condition on large scale data processing framework. Proceedings of the 2017 4th International Conference on Electrical and Electronic Engineering (ICEEE), Ankara, Turkey.","DOI":"10.1109\/ICEEE2.2017.7935834"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6509","DOI":"10.1109\/TII.2020.2966033","article-title":"Achieving Predictive and Proactive Maintenance for High-Speed Railway Power Equipment with LSTM-RNN","volume":"16","author":"Wang","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bruneo, D., and De Vita, F. (2019, January 12\u201315). On the Use of LSTM Networks for Predictive Maintenance in Smart Industries. Proceedings of the 2019 IEEE International Conference on Smart Computing (SMARTCOMP), Washington, DC, USA.","DOI":"10.1109\/SMARTCOMP.2019.00059"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dong, D., Li, X.Y., and Sun, F.Q. (2017, January 9\u201312). Life prediction of jet engines based on LSTM-recurrent neural networks. Proceedings of the 2017 Prognostics and System Health Management Conference (PHM-Harbin), Harbin, China.","DOI":"10.1109\/PHM.2017.8079264"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bampoula, X., Siaterlis, G., Nikolakis, N., and Alexopoulos, K. (2021). A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders. Sensors, 21.","DOI":"10.3390\/s21030972"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mathew, V., Toby, T., Singh, V., Rao, B.M., and Kumar, M.G. (2017, January 20\u201321). Prediction of Remaining Useful Lifetime (RUL) of turbofan engine using machine learning. Proceedings of the 2017 IEEE International Conference on Circuits and Systems (ICCS), Thiruvananthapuram, India.","DOI":"10.1109\/ICCS1.2017.8326010"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"D\u00fcd\u00fck\u00e7\u00fc, H.V., Ta\u015fk\u0131ran, M., and Kahraman, N. (2020, January 5\u20137). LSTM and WaveNet Implementation for Predictive Maintenance of Turbofan Engines. Proceedings of the 2020 IEEE 20th International Symposium on Computational Intelligence and Informatics (CINTI), Budapest, Hungary.","DOI":"10.1109\/CINTI51262.2020.9305820"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6069","DOI":"10.1109\/TII.2020.2967556","article-title":"A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders","volume":"16","author":"Essien","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Beshr, A., and Zarzoura, F. (2021). Using artificial neural networks for GNSS observations analysis and displacement prediction of suspension highway bridge. Innov. Infrastruct. Solut., 6.","DOI":"10.1007\/s41062-021-00458-4"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sak, H., Senior, A., and Beaufays, F. (2014). Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition. arXiv.","DOI":"10.21437\/Interspeech.2014-80"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, Z., Liu, Y., and Liu, S. (2017, January 26\u201328). Mechanical state prediction based on LSTM neural netwok. Proceedings of the 2017 36th Chinese Control Conference (CCC), Dalian, China.","DOI":"10.23919\/ChiCC.2017.8027963"},{"key":"ref_30","unstructured":"Ghosh, S., Vinyals, O., Strope, B., Roy, S., Dean, T., and Heck, L. (2016). Contextual LSTM (CLSTM) models for Large scale NLP tasks. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"120069","DOI":"10.1016\/j.energy.2021.120069","article-title":"A novel genetic LSTM model for wind power forecast","volume":"223","author":"Shahid","year":"2021","journal-title":"Energy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"42946","DOI":"10.1109\/ACCESS.2019.2907739","article-title":"Short-Term Abnormal Passenger Flow Prediction Based on the Fusion of SVR and LSTM","volume":"7","author":"Guo","year":"2019","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3478","DOI":"10.1109\/TII.2020.3008223","article-title":"A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life","volume":"17","author":"Ren","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"30050","DOI":"10.1109\/ACCESS.2019.2902185","article-title":"A Novel Spatio-Temporal Model for City-Scale Traffic Speed Prediction","volume":"7","author":"Niu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"106111","DOI":"10.1109\/ACCESS.2019.2930410","article-title":"Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data","volume":"7","author":"Feng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1109\/TASLP.2016.2520371","article-title":"Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval","volume":"24","author":"Palangi","year":"2016","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mao, Y., Qin, G., Ni, P., and Liu, Q. (2021). Analysis of road traffic speed in Kunming plateau mountains: A fusion PSO-LSTM algorithm. Int. J. Urban Sci., 1\u201321.","DOI":"10.1080\/12265934.2021.1882331"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s13218-015-0381-0","article-title":"Beyond Manual Tuning of Hyperparameters","volume":"29","author":"Hutter","year":"2015","journal-title":"KI K\u00fcnstliche Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102275","DOI":"10.1016\/j.scs.2020.102275","article-title":"A survey on hyperparameters optimization algorithms of forecasting models in smart grid","volume":"61","author":"Khalid","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_41","unstructured":"Hutter, F., Hoos, H., and Leyton-Brown, K. (2014, January 21\u201326). An Efficient Approach for Assessing Hyperparameter Importance. Proceedings of the 31st International Conference on Machine Learning, Beijing, China."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Park, S.H., Kim, B., Kang, C.M., Chung, C.C., and Choi, J.W. (2018, January 26\u201330). Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500658"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., G\u00fcl\u00e7ehre, \u00c7., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_44","unstructured":"Sutskever, I., Vinyals, O., and Le, Q.V. (2014). Sequence to Sequence Learning with Neural Networks. arXiv."},{"key":"ref_45","unstructured":"Wang, T., Chen, P., Amaral, K., and Qiang, J. (2016). An Experimental Study of LSTM Encoder-Decoder Model for Text Simplification. arXiv."},{"key":"ref_46","unstructured":"Bengio, S., Vinyals, O., Jaitly, N., and Shazeer, N. (2015). Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.neucom.2019.12.118","article-title":"Multivariate time series forecasting via attention-based encoder\u2013decoder framework","volume":"388","author":"Du","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Tagliaferri, R., and Marinaro, M. (2002). Applying LSTM to Time Series Predictable Through Time-Window Approaches. Neural Nets WIRN Vietri-01, Springer. Perspectives in Neural Computing.","DOI":"10.1007\/978-1-4471-0219-9"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Meng, Q., Wang, H., He, M., Gu, J., Qi, J., and Yang, L. (2020). Displacement prediction of water-induced landslides using a recurrent deep learning model. Eur. J. Environ. Civ. Eng.","DOI":"10.1080\/19648189.2020.1763847"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/TASLP.2014.2303296","article-title":"Application of Deep Belief Networks for Natural Language Understanding","volume":"22","author":"Sarikaya","year":"2014","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Sundermeyer, M., Schl\u00fcter, R., and Ney, H. (2012, January 9\u201313). LSTM neural networks for language modeling. Proceedings of the Thirteenth Annual Conference of the International Speech Communication Association, Portland, OR, USA.","DOI":"10.21437\/Interspeech.2012-65"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"34020","DOI":"10.1109\/ACCESS.2019.2896621","article-title":"An Enhanced LSTM for Trend Following of Time Series","volume":"7","author":"Hu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Gers, F.A., Schmidhuber, J., and Cummins, F. (1999, January 7\u201310). Learning to Forget: Continual Prediction with LSTM. Proceedings of the 1999 Ninth International Conference on Artificial Neural Networks ICANN 99 (Conf. Publ. No. 470), Edinburgh, UK.","DOI":"10.1049\/cp:19991218"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A Search Space Odyssey","volume":"28","author":"Greff","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.compgeo.2019.01.018","article-title":"Rigorous solution of slope stability under seismic action","volume":"109","author":"Li","year":"2019","journal-title":"Comput. Geotech."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.catcom.2019.01.024","article-title":"Stereoselective ring-opening of styrene oxide at elevated concentration by Phaseolus vulgaris epoxide hydrolase, PvEH2, in the organic\/aqueous biphasic system","volume":"123","author":"Li","year":"2019","journal-title":"Catal. Commun."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.apenergy.2018.11.063","article-title":"Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal","volume":"236","author":"Qin","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.apenergy.2019.04.047","article-title":"Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression","volume":"247","author":"Zhang","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"101157","DOI":"10.1016\/j.phycom.2020.101157","article-title":"Intelligent intrusion detection based on federated learning aided long short-term memory","volume":"42","author":"Zhao","year":"2020","journal-title":"Phys. Commun."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"114945","DOI":"10.1016\/j.apenergy.2020.114945","article-title":"Online pricing of demand response based on long short-term memory and reinforcement learning","volume":"271","author":"Kong","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Li, Y., and Lu, Y. (2019, January 21\u201322). LSTM-BA: DDoS Detection Approach Combining LSTM and Bayes. Proceedings of the 2019 Seventh International Conference on Advanced Cloud and Big Data (CBD), Suzhou, China.","DOI":"10.1109\/CBD.2019.00041"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"101588","DOI":"10.1016\/j.resourpol.2020.101588","article-title":"Multistep-ahead forecasting of coal prices using a hybrid deep learning model","volume":"65","author":"Alameer","year":"2020","journal-title":"Resour. Policy"}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/11\/13\/6101\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:24:12Z","timestamp":1760163852000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/11\/13\/6101"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,30]]},"references-count":63,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["app11136101"],"URL":"https:\/\/doi.org\/10.3390\/app11136101","relation":{},"ISSN":["2076-3417"],"issn-type":[{"type":"electronic","value":"2076-3417"}],"subject":[],"published":{"date-parts":[[2021,6,30]]}}}