{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T08:36:43Z","timestamp":1776328603450,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T00:00:00Z","timestamp":1727827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia (FCT)","award":["10.54499\/UIDB\/50022\/2020"],"award-info":[{"award-number":["10.54499\/UIDB\/50022\/2020"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia (FCT)","award":["10.54499\/UIDP\/50022\/2020"],"award-info":[{"award-number":["10.54499\/UIDP\/50022\/2020"]}]},{"name":"LAETA Programmatic Funding","award":["10.54499\/UIDB\/50022\/2020"],"award-info":[{"award-number":["10.54499\/UIDB\/50022\/2020"]}]},{"name":"LAETA Programmatic Funding","award":["10.54499\/UIDP\/50022\/2020"],"award-info":[{"award-number":["10.54499\/UIDP\/50022\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMSE"],"abstract":"<jats:p>The task of automatically and intelligently diagnosing faults in marine equipment is of great significance due to the numerous duties that shipboard professionals must handle. Incorporating automated and intelligent systems on ships allows for more efficient equipment monitoring and better decision-making. This approach has attracted considerable interest in both academia and industry because of its potential for economic savings and improved safety. Several fault diagnosis methods are documented in the literature, often involving mathematical and control theory models. However, due to the inherent complexity of some processes, not all characteristics are precisely known, making mathematical modeling highly challenging. As a result, fault diagnosis often depends on data or heuristic information. Fuzzy logic theory is particularly well suited for processing this type of information. Therefore, this paper employs fuzzy models to diagnose faults in a marine pneumatic servo-actuated valve. The fuzzy models used in fault diagnosis are obtained from the data. These fuzzy models are identified for the normal operation of the marine pneumatic servo-actuated valve, and for each fault, predicting the system\u2019s outputs from the inputs and outputs of the process. The proposed fault diagnosis framework analyzes the discrepancy signals between the outputs of the fuzzy models and the actual process outputs. These discrepancies, known as residuals, help in detecting and isolating equipment faults. The fault isolation process uses an intelligent decision-making approach to determine the specific fault in the system. This method is applied to diagnose abrupt faults in a marine pneumatic servo-actuated valve. The approach presented was used to detect and diagnose three very important faults in the operation of a marine pneumatic servo-actuated valve. The three faults were correctly detected and isolated, and no errors were detected in this detection and isolation process.<\/jats:p>","DOI":"10.3390\/jmse12101737","type":"journal-article","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T03:57:08Z","timestamp":1727841428000},"page":"1737","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Fault Diagnosis of Maritime Equipment Using an Intelligent Fuzzy Framework"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5053-8077","authenticated-orcid":false,"given":"L. F.","family":"Mendon\u00e7a","sequence":"first","affiliation":[{"name":"ENIDH\u2014Escola Superior N\u00e1utica Infante D. Henrique, 2770-058 Pa\u00e7o de Arcos, Portugal"},{"name":"IDMEC\u2014Instituto de Engenharia Mec\u00e2nica, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8030-4746","authenticated-orcid":false,"given":"J. M. C.","family":"Sousa","sequence":"additional","affiliation":[{"name":"IDMEC\u2014Instituto de Engenharia Mec\u00e2nica, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7961-1004","authenticated-orcid":false,"given":"S. M.","family":"Vieira","sequence":"additional","affiliation":[{"name":"IDMEC\u2014Instituto de Engenharia Mec\u00e2nica, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.oceaneng.2017.11.017","article-title":"Predicting ship machinery system condition through analytical reliability tools and artificial neural networks","volume":"152","author":"Lazakis","year":"2018","journal-title":"Ocean Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1080\/17445302.2018.1500189","article-title":"Investigating an SVM-driven, one-class approach to estimating ship systems","volume":"14","author":"Lazakis","year":"2018","journal-title":"Ships Offshore Struct."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1080\/17445302.2018.1443694","article-title":"Using artificial neural network-self-organising map for data clustering of marine engine condition monitoring applications","volume":"13","author":"Raptodimos","year":"2018","journal-title":"Ships Offshore Struct."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"109723","DOI":"10.1016\/j.oceaneng.2021.109723","article-title":"Multi-label classification for simultaneous fault diagnosis of marine machinery: A comparative study","volume":"239","author":"Tan","year":"2021","journal-title":"Ocean Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/0005-1098(84)90098-0","article-title":"Process fault detection based on modelling and estimation methods: A survey","volume":"20","author":"Isermann","year":"1984","journal-title":"Automatica"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, R., and Patton, R. (1999). Robust Model-Based Fault Diagnosis for Dynamic Systems, Kluwer Academic Publishers.","DOI":"10.1007\/978-1-4615-5149-2"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1687814015624834","DOI":"10.1177\/1687814015624834","article-title":"Rolling bearing fault diagnosis method based on data-driven random fuzzy evidence acquisition and Dempster\u2013Shafer evidence theory","volume":"8","author":"Sun","year":"2016","journal-title":"Adv. Mech. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9714","DOI":"10.1109\/JIOT.2020.2993411","article-title":"Internet of ships: A survey on architectures, emerging applications, and challenges","volume":"7","author":"Aslam","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_9","unstructured":"Beard, R.V. (1971). Failure Accomodation in Linear System through Self-Reorganization. [Ph.D. Thesis, Massachusetts Institute of Technology]. Available online: https:\/\/dspace.mit.edu\/handle\/1721.1\/16415."},{"key":"ref_10","unstructured":"Rault, A., Richalet, A., Barbot, A., and Sergenton, J.P. (1971, January 6\u201310). Identification and modelling of a jet engine. Proceedings of the IFAC Symposium of Digital Simulation of Continuous Processes, Gejor, Gy\u00f6r, Hungary."},{"key":"ref_11","unstructured":"Siebert, H., and Isermann, R. (1976). Fault Diagnosis Via On-Line Correlation Analysis, VDI-VDE. (In German)."},{"key":"ref_12","unstructured":"Himmelblau, D.M. (1978). Fault Diagnosis in Chemical and Petrochemical Processes, Elsevier."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/0167-6911(90)90094-B","article-title":"Fault detection via factorization approach","volume":"14","author":"Ding","year":"2000","journal-title":"Syst. Control Lett."},{"key":"ref_14","first-page":"625479","article-title":"Intelligent fault diagnosis of aero-engine high-speed bearings using enhanced CNN","volume":"43","author":"Han","year":"2022","journal-title":"Acta Aeronaut. Astronaut. Sin."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"104099","DOI":"10.1016\/j.compind.2024.104099","article-title":"A high-accuracy intelligent fault diagnosis method for aero-engine bearings with limited samples","volume":"159\u2013160","author":"Wang","year":"2024","journal-title":"Comput. Ind."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, J., Yang, Z., Zhou, X., Song, C., and Wu, Y. (2024). Advancing the Diagnosis of Aero-Engine Bearing Faults with Rotational Spectrum and Scale-Aware Robust Network. Aerospace, 11.","DOI":"10.3390\/aerospace11080613"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Guo, Y., and Zhang, J. (2023). Fault diagnosis of marine diesel engines under partial set and cross working conditions based on transfer learning. J. Mar. Sci. Eng., 11.","DOI":"10.3390\/jmse11081527"},{"key":"ref_18","first-page":"779","article-title":"Fault diagnosis of ship power equipment based on adaptive neural network","volume":"23","author":"Zhang","year":"2022","journal-title":"Int. J. Emerg. Electr. Power Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"118814","DOI":"10.1016\/j.eswa.2022.118814","article-title":"Hierarchical level fault detection and diagnosis of ship engine systems","volume":"213","author":"Kang","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yin, Y., Xu, F., and Pang, B. (2022). Online intelligent fault diagnosis of redundant sensors in PWR based on artificial neural network. Front. Energy Res., 10.","DOI":"10.3389\/fenrg.2022.1011362"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/S0967-0661(97)00053-1","article-title":"Trends in the application of model-based fault detection and diagnosis of technical processes","volume":"5","author":"Isermann","year":"1997","journal-title":"Control Eng. Pract."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/S1474-6670(17)36468-6","article-title":"Fault diagnosis based on analytical models for linear and nonlinear systems\u2014A tutorial","volume":"36","author":"Kinnaert","year":"2003","journal-title":"IFAC Proc. Vol."},{"key":"ref_23","first-page":"347","article-title":"Distributed model-based sensor fault diagnosis of marine fuel engines","volume":"55","author":"Kougiatsos","year":"2022","journal-title":"IFAC-Pap."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1016\/S0967-0661(00)00064-2","article-title":"Model-based fault detection and isolation for a gas\u2013liquid separation unit","volume":"8","author":"Kinnaert","year":"2000","journal-title":"Control Eng. Pract."},{"key":"ref_25","unstructured":"Gertler, J. (1998). Fault Detection and Diagnosis in Engineering Systems, Marcel Dekker."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"116798","DOI":"10.1016\/j.oceaneng.2024.116798","article-title":"Fault detection and diagnosis of marine diesel engines: A systematic review","volume":"294","author":"Lv","year":"2024","journal-title":"Ocean Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sousa, J.M., and Kaymak, U. (2002). Fuzzy Decision Making in Modeling and Control, World Scientific Pub. Co.","DOI":"10.1142\/9789812777911"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/TSMC.1985.6313399","article-title":"Fuzzy identification of systems and its applications to modelling and control","volume":"15","author":"Takagi","year":"1985","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Babu\u0161ka, R. (1998). Fuzzy Modeling for Control, Kluwer Academic Publishers.","DOI":"10.1007\/978-94-011-4868-9"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gustafson, D.E., and Kessel, W.C. (1979, January 10\u201312). Fuzzy clustering with a fuzzy covariance matrix. Proceedings of the 1978 IEEE Conference on Decision and Control Including the 17th Symposium on Adaptive Processes, San Diego, CA, USA.","DOI":"10.1109\/CDC.1978.268028"}],"container-title":["Journal of Marine Science and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-1312\/12\/10\/1737\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:08:59Z","timestamp":1760112539000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-1312\/12\/10\/1737"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,2]]},"references-count":30,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["jmse12101737"],"URL":"https:\/\/doi.org\/10.3390\/jmse12101737","relation":{},"ISSN":["2077-1312"],"issn-type":[{"value":"2077-1312","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,2]]}}}