{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T08:22:12Z","timestamp":1781252532373,"version":"3.54.1"},"reference-count":21,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science, Technology, and Innovation of Colombia","award":["82913"],"award-info":[{"award-number":["82913"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Systems"],"abstract":"<jats:p>This work presents the development of an intelligent system designed to support the predictive maintenance of the Colombian Navy\u2019s maritime vessels through the estimation of remaining useful life and unsupervised anomaly detection, within the framework of the project called \u201cColombian Integrated Platform Supervision and Control System\u201d (SISCP-C). This project seeks to guarantee the sustainability of the vessels over time, increase their operational availability, and optimize their life cycle cost, in accordance with the institution\u2019s strategic direction established in the Naval Development Plan 2042. The system provides useful information to the crew, enabling informed decision-making for intelligent and efficient maintenance strategies. To address the limited availability of normal operating data, synthetic data generation techniques with seeding are implemented, including tabular variational autoencoders, conditional tabular generative adversarial networks, and Gaussian copulas. Among these, tabular variational autoencoders achieved the best performance and are used to generate synthetic datasets under normal conditions for the W\u00e4rtsil\u00e4 6L26 diesel engine (manufactured by W\u00e4rtsil\u00e4 Italia S.p.A., Trieste, Italy). These datasets are used to train several unsupervised anomaly detection models, including one-class support vector machines, classical autoencoders, and long short-term memory-based autoencoders. The long short-term memory autoencoders outperformed the others in terms of detection metrics. Dedicated multivariate long short-term memory autoencoders are subsequently trained for each engine subsystem. By calculating the mean absolute error of the reconstructions, a subsystem-specific health index is computed, which is used to estimate the remaining useful life.<\/jats:p>","DOI":"10.3390\/systems13100845","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T09:35:49Z","timestamp":1758879349000},"page":"845","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Naval AI-Based Utility for Remaining Useful Life Prediction and Anomaly Detection for Lifecycle Management"],"prefix":"10.3390","volume":"13","author":[{"given":"Carlos E.","family":"Pardo B.","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081007, Atl\u00e1ntico, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0017-0976","authenticated-orcid":false,"given":"Oscar I.","family":"Iglesias R.","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081007, Atl\u00e1ntico, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maicol D.","family":"Le\u00f3n A.","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081007, Atl\u00e1ntico, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0918-9375","authenticated-orcid":false,"given":"Christian G.","family":"Quintero M.","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081007, Atl\u00e1ntico, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel Andr\u00e9s","family":"Garnica L\u00f3pez","sequence":"additional","affiliation":[{"name":"Naval Science and Technology Directorate, DICYT, JINEN, Bogot\u00e1 111011, Cundinamarca, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9s Ricardo","family":"Pedraza Leguizam\u00f3n","sequence":"additional","affiliation":[{"name":"Naval Technological Development Center, CEDNAV, Naval Base, Cartagena 130001, Bol\u00edvar, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108340","DOI":"10.1016\/j.engappai.2024.108340","article-title":"A Conceptual Framework for Machine Learning Algorithm Selection for Predictive Maintenance","volume":"133","author":"Arena","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Simion, D., Postolache, F., Fleac\u0103, B., and Fleac\u0103, E. (2024). AI-Driven Predictive Maintenance in Modern Maritime Transport\u2014Enhancing Operational Efficiency and Reliability. Appl. Sci., 14.","DOI":"10.20944\/preprints202409.0930.v1"},{"key":"ref_3","unstructured":"Due\u00f1as-Ram\u00edrez, L.M., Casta\u00f1o-Restrepo, C.A., Castiblanco-Tique, S., and Villegas-L\u00f3pez, G.A. (,  2020). Success Stories Concerning the Implementation of Predictive Maintenance Through the Use of Industry 4.0 Technologies in Colombian Companies. Proceedings of the 3rd International Congress on Systems Engineering, Lima, Peru."},{"key":"ref_4","unstructured":"Tadayon, M., and Pottie, G. (2020). TsBNgen: A Python Library to Generate Time Series Data from an Arbitrary Dynamic Bayesian Network Structure. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Patki, N., Wedge, R., and Veeramachaneni, K. (2016, January 17\u201319). The Synthetic Data Vault. Proceedings of the 3rd IEEE International Conference on Data Science and Advanced Analytics (DSAA), Montreal, QC, Canada.","DOI":"10.1109\/DSAA.2016.49"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Baressi \u0160egota, S., Mrzljak, V., An\u0111eli\u0107, N., Poljak, I., and Car, Z. (2023). Use of Synthetic Data in Maritime Applications for the Problem of Steam Turbine Exergy Analysis. J. Mar. Sci. Eng., 11.","DOI":"10.3390\/jmse11081595"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mohakul, D., Kumar, C.R.S., Singh, S., Katti, S., and Chougule, S. (2023, January 5\u20137). Health Monitoring of Ship\u2019s Engine with Simulated Data by Using Classifiers -Preliminary Result. Proceedings of the 2023 2nd International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), Tiruchirappalli, India.","DOI":"10.1109\/ICEEICT56924.2023.10157555"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"109204","DOI":"10.1016\/j.ress.2023.109204","article-title":"Maintenance Optimization in Industry 4.0","volume":"234","author":"Pinciroli","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_9","first-page":"182","article-title":"A Review of Predictive Maintenance Approaches for Corrosion Detection and Maintenance of Marine Structures","volume":"19","author":"Ali","year":"2024","journal-title":"J. Sustain. Sci. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106889","DOI":"10.1016\/j.cie.2020.106889","article-title":"Predictive Maintenance in the Industry 4.0: A Systematic Literature Review","volume":"150","author":"Zonta","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kalafatelis, A.S., Nomikos, N., Giannopoulos, A., and Trakadas, P. (2024). A Survey on Predictive Maintenance in the Maritime Industry Using Machine and Federated Learning. Authorea Preprints.","DOI":"10.36227\/techrxiv.173473250.04784922\/v1"},{"key":"ref_12","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":"2015","journal-title":"IEEE Trans. Industr. Inform."},{"key":"ref_13","unstructured":"Kalafatelis, A. (2024, January 2\u20133). A Lightweight Predictive Maintenance Strategy for Marine HFO Purification Systems. Proceedings of the 21 st European, Mediterranean, and Middle Eastern Conference on Information Systems, Athens, Greece."},{"key":"ref_14","first-page":"3","article-title":"A Literature Review and Future Research Agenda on Fault Detection and Diagnosis Studies in Marine Machinery Systems","volume":"238","author":"Orhan","year":"2024","journal-title":"Proc. Inst. Mech. Eng. Part M J. Eng. Marit. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"117634","DOI":"10.1016\/j.eswa.2022.117634","article-title":"RADIS: A Real-Time Anomaly Detection Intelligent System for Fault Diagnosis of Marine Machinery","volume":"204","author":"Lazakis","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Githinji, S., and Maina, C.W. (2023, January 20\u201322). Anomaly Detection on Time Series Sensor Data Using Deep LSTM-Autoencoder. Proceedings of the IEEE Africon Conference, Nairobi, Kenya.","DOI":"10.1109\/AFRICON55910.2023.10293676"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Faggioni Nicolo, N., Caviglia, A., Guarnera, N., Schinin\u00e0, E., Sansebastiano, E., and Chiti, R. (2024, January 5\u20137). Enhancing Predictive Maintenance in the Maritime Industry with Unsupervised Learning. Proceedings of the International Ship Control Systems Symposium, Liverpool, UK.","DOI":"10.24868\/11149"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lachekhab, F., Benzaoui, M., Tadjer, S.A., Bensmaine, A., and Hamma, H. (2024). LSTM-Autoencoder Deep Learning Model for Anomaly Detection in Electric Motor. Energies, 17.","DOI":"10.3390\/en17102340"},{"key":"ref_19","unstructured":"DataCebo, Inc. (2023). Synthetic Data Metrics, DataCebo, Inc."},{"key":"ref_20","unstructured":"Palacio-Ni\u00f1o, J.-O., and Berzal, F. (2019). Evaluation Metrics for Unsupervised Learning Algorithms. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1177\/0004563221992088","article-title":"Best Practice in Statistics: Use the Welch t-Test When Testing the Difference between Two Groups","volume":"58","author":"West","year":"2021","journal-title":"Ann. Clin. Biochem."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/10\/845\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T09:48:19Z","timestamp":1758880099000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/10\/845"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,26]]},"references-count":21,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["systems13100845"],"URL":"https:\/\/doi.org\/10.3390\/systems13100845","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,26]]}}}