{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T22:24:09Z","timestamp":1776205449904,"version":"3.50.1"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030990749","type":"print"},{"value":"9783030990756","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,9,18]],"date-time":"2022-09-18T00:00:00Z","timestamp":1663459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,9,18]],"date-time":"2022-09-18T00:00:00Z","timestamp":1663459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-030-99075-6_24","type":"book-chapter","created":{"date-parts":[[2022,9,17]],"date-time":"2022-09-17T16:18:45Z","timestamp":1663431525000},"page":"281-291","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Prediction of Sensor Values in Paper Pulp Industry Using Neural Networks"],"prefix":"10.1007","author":[{"given":"Jo\u00e3o Antunes","family":"Rodrigues","sequence":"first","affiliation":[]},{"given":"Jos\u00e9 Torres","family":"Farinha","sequence":"additional","affiliation":[]},{"given":"Ant\u00f3nio Marques","family":"Cardoso","sequence":"additional","affiliation":[]},{"given":"Mateus","family":"Mendes","sequence":"additional","affiliation":[]},{"given":"Ricardo","family":"Mateus","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,18]]},"reference":[{"key":"24_CR1","doi-asserted-by":"publisher","unstructured":"Cachada, A., et al.: Maintenance 4.0: intelligent and predictive maintenance system architecture. In: 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), Sept 2018, vol. 1, pp. 139\u2013146 (2018). https:\/\/doi.org\/10.1109\/ETFA.2018.8502489","DOI":"10.1109\/ETFA.2018.8502489"},{"issue":"3","key":"24_CR2","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1108\/JQME-05-2013-0029","volume":"19","author":"U Kumar","year":"2013","unstructured":"Kumar, U., Galar, D., Parida, A., Stenstr\u00f6m, C., Berges, L.: Maintenance performance metrics: a state-of-the-art review. J. Qual. Maintenance Eng. 19(3), 233\u2013277 (2013). https:\/\/doi.org\/10.1108\/JQME-05-2013-0029","journal-title":"J. Qual. Maintenance Eng."},{"key":"24_CR3","doi-asserted-by":"publisher","unstructured":"Selcuk, S.: Predictive maintenance, its implementation and latest trends. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 231(9), 1670\u20131679 (2017). https:\/\/doi.org\/10.1177\/0954405415601640","DOI":"10.1177\/0954405415601640"},{"key":"24_CR4","doi-asserted-by":"publisher","unstructured":"Martins, A.B., Torres Farinha, J., Marques Cardoso, A.: Calibration and certification of industrial sensors\u2014a global review. WSEAS Trans. Syst. Control 15, 394\u2013416 (2020). https:\/\/doi.org\/10.37394\/23203.2020.15.41","DOI":"10.37394\/23203.2020.15.41"},{"issue":"3","key":"24_CR5","first-page":"9","volume":"22","author":"JA Rodrigues","year":"2020","unstructured":"Rodrigues, J., Costa, I., Farinha, J.T., Mendes, M., Margalho, L.: Predicting motor oil condition using artificial neural networks and principal component analysis. Sci. Technol. 22(3), 9 (2020)http:\/\/ein.org.pl\/sites\/default\/files\/2020-03-06.pdf","journal-title":"Sci. Technol."},{"key":"24_CR6","doi-asserted-by":"publisher","unstructured":"Allah Bukhsh, Z., Saeed, A., Stipanovic, I., Doree, A.G.: Predictive maintenance using tree-based classification techniques: a case of railway switches. Transp. Res. Part C Emerg. Technol. 101, 35\u201354 (2019). https:\/\/doi.org\/10.1016\/j.trc.2019.02.001","DOI":"10.1016\/j.trc.2019.02.001"},{"key":"24_CR7","doi-asserted-by":"publisher","unstructured":"Hongxiang, T., Yuntao, L., Xiangjun, W.: Application of neural network to diesel engine SOA. In: 2011 Third international conference on measuring technology and mechatronics automation, Jan 2011, vol. 1, pp. 555\u2013558. https:\/\/doi.org\/10.1109\/ICMTMA.2011.141","DOI":"10.1109\/ICMTMA.2011.141"},{"key":"24_CR8","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.procir.2016.09.033","volume":"59","author":"C Okoh","year":"2017","unstructured":"Okoh, C., Roy, R., Mehnen, J.: Predictive maintenance modelling for through-life engineering services. Procedia CIRP 59, 196\u2013201 (2017). https:\/\/doi.org\/10.1016\/j.procir.2016.09.033","journal-title":"Procedia CIRP"},{"key":"24_CR9","doi-asserted-by":"publisher","unstructured":"Makridis, G., Kyriazis, D., Plitsos, S.: Predictive maintenance leveraging machine learning for time-series forecasting in the maritime industry. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Sept 2020, pp. 1\u20138. https:\/\/doi.org\/10.1109\/ITSC45102.2020.9294450","DOI":"10.1109\/ITSC45102.2020.9294450"},{"issue":"2","key":"24_CR10","doi-asserted-by":"publisher","first-page":"489","DOI":"10.9770\/jesi.2018.6.2(2)","volume":"6","author":"AI Vlasov","year":"2018","unstructured":"Vlasov, A.I., Grigoriev, P.V., Krivoshein, A.I., Shakhnov, V.A., Filin, S.S., Migalin, V.S.: Smart management of technologies: predictive maintenance of industrial equipment using wireless sensor networks. Entrepreneurship Sustain. Issues 6(2), 489\u2013502 (2018). https:\/\/doi.org\/10.9770\/jesi.2018.6.2(2)","journal-title":"Entrepreneurship Sustain. Issues"},{"key":"24_CR11","doi-asserted-by":"publisher","unstructured":"Fernandes, M., Canito, A., Corchado, J.M., Marreiros, G.: Fault detection mechanism of a predictive maintenance system based on autoregressive integrated moving average models. In: Distributed Computing and Artificial Intelligence, 16th International Conference, Cham, pp. 171\u2013180 (2020). https:\/\/doi.org\/10.1007\/978-3-030-23887-2_20","DOI":"10.1007\/978-3-030-23887-2_20"},{"key":"24_CR12","doi-asserted-by":"publisher","unstructured":"Mateus, B.C., Mendes, M., Farinha, J.T., Cardoso, A.M.: Anticipating future behavior of an industrial press using LSTM networks. Appl. Sci. 11(13) (2021). https:\/\/doi.org\/10.3390\/app11136101","DOI":"10.3390\/app11136101"},{"key":"24_CR13","unstructured":"sklearn.neural_network.MLPRegressor\u2014scikit-learn 0.24.2 documentation. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.neural_network.MLPRegressor.html. Accessed 16 May 2021"}],"container-title":["Mechanisms and Machine Science","Proceedings of IncoME-VI and TEPEN 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-99075-6_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,17]],"date-time":"2022-09-17T16:30:24Z","timestamp":1663432224000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-99075-6_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,18]]},"ISBN":["9783030990749","9783030990756"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-99075-6_24","relation":{},"ISSN":["2211-0984","2211-0992"],"issn-type":[{"value":"2211-0984","type":"print"},{"value":"2211-0992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,18]]},"assertion":[{"value":"18 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}