{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T23:13:46Z","timestamp":1790723626385,"version":"4.1.0"},"reference-count":45,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T00:00:00Z","timestamp":1611964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.<\/jats:p>","DOI":"10.3390\/s21030932","type":"journal-article","created":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T08:56:01Z","timestamp":1611996961000},"page":"932","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":123,"title":["Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks"],"prefix":"10.3390","volume":"21","author":[{"given":"Ziqiu","family":"Kang","sequence":"first","affiliation":[{"name":"Information Technology Group, Wageningen University &amp; Research, 6706 KN Wageningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0959-2930","authenticated-orcid":false,"given":"Cagatay","family":"Catal","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8538-7261","authenticated-orcid":false,"given":"Bedir","family":"Tekinerdogan","sequence":"additional","affiliation":[{"name":"Information Technology Group, Wageningen University &amp; Research, 6706 KN Wageningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3382","DOI":"10.1016\/j.ins.2008.05.007","article-title":"Integrated production planning and preventive maintenance in deteriorating production systems","volume":"178","author":"Aghezzaf","year":"2008","journal-title":"Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.conengprac.2018.04.011","article-title":"Impact of condition based maintenance policies on the service level of multi-stage manufacturing systems","volume":"76","author":"Angius","year":"2018","journal-title":"Control. Eng. Pract."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1108\/13552511111116259","article-title":"Cost of poor maintenance","volume":"17","author":"Salonen","year":"2011","journal-title":"J. Qual. Maint. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.ress.2016.10.002","article-title":"The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance","volume":"158","author":"Teunter","year":"2017","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1002\/mcda.1618","article-title":"Maintenance strategy selection with risky evaluations using RAHP","volume":"24","author":"Seiti","year":"2017","journal-title":"J. Multi-Criteria Decis. Anal."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1016\/j.promfg.2020.01.128","article-title":"Implementing TPM supported by 5S to improve the availability of an automotive production line","volume":"38","author":"Ribeiro","year":"2019","journal-title":"Procedia Manuf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.ijpe.2009.05.005","article-title":"Maintenance practices in Swedish industries: Survey results","volume":"121","author":"Alsyouf","year":"2009","journal-title":"Int. J. Prod. Econ."},{"key":"ref_8","first-page":"1175","article-title":"A new model based on Artificial Bee Colony algorithm for preventive maintenance with replacement scheduling in continuous production lines","volume":"22","author":"Ozcan","year":"2019","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/S0925-5273(97)00054-6","article-title":"Setting preventive maintenance schedules when data are sparse","volume":"51","author":"Percy","year":"1997","journal-title":"Int. J. Prod. Econ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.jmsy.2017.08.005","article-title":"Estimation of active maintenance opportunity windows in Bernoulli production lines","volume":"45","author":"Gu","year":"2017","journal-title":"J. Manuf. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.ress.2016.05.006","article-title":"Methodologies for system-level remaining useful life prediction","volume":"154","author":"Khorasgani","year":"2016","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1109\/TSM.2012.2209131","article-title":"A predictive maintenance system for epitaxy processes based on filtering and prediction techniques","volume":"25","author":"Susto","year":"2012","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mobley, R.K. (2002). An Introduction to Predictive Maintenance, Elsevier.","DOI":"10.1016\/B978-075067531-4\/50006-3"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1016\/j.epsr.2005.09.010","article-title":"Optimum maintenance policy with Markov processes","volume":"76","author":"Chan","year":"2006","journal-title":"Electr. Power Syst. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"106237","DOI":"10.1016\/j.epsr.2020.106237","article-title":"Monte Carlo simulation model to coordinate the preventive maintenance scheduling of generating units in isolated distributed Power Systems","volume":"182","author":"Duarte","year":"2020","journal-title":"Electr. Power Syst. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3823","DOI":"10.1080\/00207540701829752","article-title":"Throughput analysis of production systems: Recent advances and future topics","volume":"47","author":"Li","year":"2009","journal-title":"Int. J. Prod. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.epsr.2004.03.007","article-title":"A condition-based preventive maintenance arrangement for thermal power plants","volume":"72","author":"Yang","year":"2004","journal-title":"Electr. Power Syst. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.ijpe.2006.08.005","article-title":"Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process","volume":"107","author":"Wang","year":"2007","journal-title":"Int. J. Prod. Econ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.cie.2019.01.032","article-title":"Exact and heuristic approaches for joint maintenance and spare parts planning","volume":"129","author":"Bakal","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106273","DOI":"10.1016\/j.cie.2020.106273","article-title":"Joint optimization of production and maintenance strategies considering a dynamic sampling strategy for a deteriorating system","volume":"140","author":"Gharbi","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"105559","DOI":"10.1016\/j.asoc.2019.105559","article-title":"Developing a novel risk-based MCDM approach based on D numbers and fuzzy information axiom and its applications in preventive maintenance planning","volume":"82","author":"Seiti","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.jmsy.2019.01.002","article-title":"Maintenance grouping optimization with system multi-level information based on BN lifetime prediction model","volume":"50","author":"Wang","year":"2019","journal-title":"J. Manuf. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1109\/TII.2014.2349359","article-title":"Machine learning for predictive maintenance: A multiple classifier approach","volume":"11","author":"Susto","year":"2014","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Luo, R.C., and Wang, H. (2018, January 27\u201330). Diagnostic and Prediction of Machines Health Status as Exemplary Best Practice for Vehicle Production System. Proceedings of the 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), Chicago, IL, USA.","DOI":"10.1109\/VTCFall.2018.8690710"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.procs.2015.07.322","article-title":"Evaluation of Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet","volume":"53","author":"Fan","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_26","unstructured":"Saxena, A., and Goebel, K. (2020, November 20). Turbofan Engine Degradation Simulation Data Set, Available online: https:\/\/ti.arc.nasa.gov\/tech\/dash\/groups\/pcoe\/prognostic-data-repository\/#turbofan."},{"key":"ref_27","first-page":"1","article-title":"Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets","volume":"5","author":"Ramasso","year":"2014","journal-title":"Int. J. Progn. Health Manag."},{"key":"ref_28","unstructured":"Borst, N.G. (2020). Adaptations for CNN-LSTM Network for Remaining Useful Life Prediction: Adaptable Time Window and Sub-Network Training. [Master\u2019s Thesis, Delft University of Technology]."},{"key":"ref_29","unstructured":"Garga, A.K., McClintic, K.T., Campbell, R.L., Yang, C.C., Lebold, M.S., Hay, T.A., and Byington, C.S. (2001, January 10\u201317). Hybrid reasoning for prognostic learning in CBM systems. Proceedings of the 2001 IEEE Aerospace Conference Proceedings (Cat. No. 01TH8542), Big Sky, MT, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1016\/j.ymssp.2010.11.018","article-title":"Prognostic modelling options for remaining useful life estimation by industry","volume":"25","author":"Sikorska","year":"2011","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1007\/s13198-013-0195-0","article-title":"Remaining useful life estimation","volume":"5","author":"Ahmadzadeh","year":"2014","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.procir.2014.02.006","article-title":"Overview of Remaining Useful Life Prediction Techniques in Through-life Engineering Services","volume":"16","author":"Okoh","year":"2014","journal-title":"Procedia CIRP"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s10033-019-0317-y","article-title":"Remaining useful life model and assessment of mechanical products: A brief review and a note on the state space model method","volume":"32","author":"Hu","year":"2019","journal-title":"Chin. J. Mech. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejor.2010.11.018","article-title":"Remaining useful life estimation\u2013a review on the statistical data driven approaches","volume":"213","author":"Si","year":"2011","journal-title":"Eur. J. Oper. Res."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Djeziri, M.A., Benmoussa, S., and Zio, E. (2020). Review on Health Indices Extraction and Trend Modeling for Remaining Useful Life Estimation. Artificial Intelligence Techniques for a Scalable Energy Transition, Springer.","DOI":"10.1007\/978-3-030-42726-9_8"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"106113","DOI":"10.1016\/j.asoc.2020.106113","article-title":"Remaining useful life prediction using multi-scale deep convolutional neural network","volume":"89","author":"Li","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"107788","DOI":"10.1016\/j.measurement.2020.107788","article-title":"Similarity-Based Deep Learning Approach for Remaining Useful Life Prediction","volume":"159","author":"Hou","year":"2020","journal-title":"Measurement"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"108286","DOI":"10.1016\/j.measurement.2020.108286","article-title":"Transferable convolutional neural network based remaining useful life prediction of bearing under multiple failure behaviors","volume":"168","author":"Cheng","year":"2020","journal-title":"Measurement"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.neucom.2019.10.064","article-title":"Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery","volume":"379","author":"Wang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"105919","DOI":"10.1016\/j.asoc.2019.105919","article-title":"A novel deep learning method based on attention mechanism for bearing remaining useful life prediction","volume":"86","author":"Chen","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.isatra.2019.07.004","article-title":"Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network","volume":"97","author":"Wu","year":"2020","journal-title":"ISA Trans."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"105843","DOI":"10.1016\/j.knosys.2020.105843","article-title":"Data alignments in machinery remaining useful life prediction using deep adversarial neural networks","volume":"197","author":"Li","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"ref_43","first-page":"34","article-title":"Remaining useful life prediction via a variational autoencoder and a time-window-based sequence neural network","volume":"37","author":"Su","year":"2020","journal-title":"Qual. Reliab. Eng. Int."},{"key":"ref_44","first-page":"52","article-title":"Evaluation of neural networks in the subject of prognostics as compared to linear regression model","volume":"10","author":"Riad","year":"2010","journal-title":"Int. J. Eng. Technol."},{"key":"ref_45","first-page":"157","article-title":"Random Forests","volume":"45","author":"Cutler","year":"2011","journal-title":"Mach. Learn."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/932\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:17:40Z","timestamp":1760159860000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/932"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,30]]},"references-count":45,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21030932"],"URL":"https:\/\/doi.org\/10.3390\/s21030932","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,30]]}}}