{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T18:32:15Z","timestamp":1775241135330,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T00:00:00Z","timestamp":1629676800000},"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>The ability to exploit data for obtaining useful and actionable information and for providing insights is an essential element for continuous process improvements. Recognizing the value of data as an asset, marine engineering puts data considerations at the core of system design. Used wisely, data can help the shipping sector to achieve operating cost savings and efficiency increase, higher safety, wellness of crew rates, and enhanced environmental protection and security of assets. The main goal of this study is to develop a methodology able to harmonize data collected from various sensors onboard and to implement a scalable and responsible artificial intelligence framework, to recognize patterns that indicate early signs of defective behavior in the operational state of the vessel. Specifically, the methodology examined in the present study is based on a 1D Convolutional Neural Network (CNN) being fed time series directly from the available dataset. For this endeavor, the dataset undergoes a preprocessing procedure. Aspiring to determine the effect of the parameters composing the networks and the values that ensure the best performance, a parametric inquiry is presented, determining the impact of the input period and the degree of degradation that our models identify adequately. The results provide an insightful picture of the applicability of 1D-CNN models in performing condition monitoring in ships, which is not thoroughly examined in the maritime sector for condition monitoring. The data modeling along with the development of the neural networks was undertaken with the Python programming language.<\/jats:p>","DOI":"10.3390\/s21165658","type":"journal-article","created":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T04:22:20Z","timestamp":1629692540000},"page":"5658","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["A Deep Learning-Based Fault Detection Model for Optimization of Shipping Operations and Enhancement of Maritime Safety"],"prefix":"10.3390","volume":"21","author":[{"given":"Panayiotis","family":"Theodoropoulos","sequence":"first","affiliation":[{"name":"Prisma Electronics SA, Leof. Poseidonos 42, 17675 Kallithea, Greece"},{"name":"Department of Mechanical Engineering and Aeronautic, University of Patras, 26504 Patras, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2413-595X","authenticated-orcid":false,"given":"Christos C.","family":"Spandonidis","sequence":"additional","affiliation":[{"name":"Prisma Electronics SA, Leof. Poseidonos 42, 17675 Kallithea, Greece"}]},{"given":"Fotis","family":"Giannopoulos","sequence":"additional","affiliation":[{"name":"Prisma Electronics SA, Leof. Poseidonos 42, 17675 Kallithea, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6679-8690","authenticated-orcid":false,"given":"Spilios","family":"Fassois","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering and Aeronautic, University of Patras, 26504 Patras, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,23]]},"reference":[{"key":"ref_1","unstructured":"United Nations (2019). Review of Maritime Transport, United Nations Conference On Trade And Development."},{"key":"ref_2","unstructured":"Panteia, Significance, PwC, University of Antwerp, and Maritime-Insight (2015). Study on the Analysis and Evolution of International and EU Shipping, University of Antwerp. Final Report."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/S0925-5273(01)00156-6","article-title":"A framework for maintenance concept development","volume":"77","author":"Waeyenbergh","year":"2002","journal-title":"Int. J. Prod. Econ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mobley, R.K. (2002). An Introduction to Predictive Maintenance, Plant Engineering, Butterworth-Heinemann.","DOI":"10.1016\/B978-075067531-4\/50016-6"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.oceaneng.2017.12.002","article-title":"Condition-Based Maintenance of Naval Propulsion Systems with supervised Data Analysis","volume":"149","author":"Cipollini","year":"2018","journal-title":"Ocean Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chenarlogh, V.A., Razzazi, F., and Mohammadyahya, N. (2019, January 18\u201319). A Multi-View Human Action Recognition System in Limited Data Case using Multi-Stream CNN. Proceedings of the 2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), Shahrood, Iran.","DOI":"10.1109\/ICSPIS48872.2019.9066079"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1007\/s11265-020-01597-0","article-title":"CVR: A Continuously Variable Rate LDPC Decoder Using Parity Check Extension for Minimum Latency","volume":"93","author":"Pourjabar","year":"2021","journal-title":"J. Signal Process. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"101804","DOI":"10.1016\/j.flowmeasinst.2020.101804","article-title":"Application of GMDH neural network technique to improve measuring precision of a simplified photon attenua-tion based two-phase flowmeter","volume":"75","author":"Roshani","year":"2020","journal-title":"Flow Meas. Instrum."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fathabadi, F.R., Grantner, J.L., Shebrain, S.A., and Abdel-Qader, I. (2021, January 21\u201323). Multi-Class Detection of Laparoscopic Instruments for the Intelligent Box-Trainer System Using Faster R-CNN Architecture. Proceedings of the 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), Herl\u2019any, Slovakia.","DOI":"10.1109\/SAMI50585.2021.9378617"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Voghoei, S., Tonekaboni, N.H., Wallace, J.G., and Arabnia, H.R. (2018, January 12\u201314). Deep Learning at the Edge. Proceedings of the 2018 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.","DOI":"10.1109\/CSCI46756.2018.00177"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2113","DOI":"10.1007\/s13204-021-01949-7","article-title":"Smart tracking of the influence of alu-mina nanoparticles on the thermal coefficient of nanosuspensions: Application of LS-SVM methodology","volume":"11","author":"Nabavi","year":"2021","journal-title":"Appl. Nanosci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1109\/TASE.2020.2983061","article-title":"Data-Driven Approach for Fault Detection and Diagnostic in Semiconductor Manufacturing","volume":"17","author":"Fan","year":"2020","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101166","DOI":"10.1016\/j.aei.2020.101166","article-title":"Defective wafer detection using a denoising autoencoder for semiconductor manufacturing processes","volume":"46","author":"Fan","year":"2020","journal-title":"Adv. Eng. Inform."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Park, Y.-J., Fan, S.-K.S., and Hsu, C.-Y. (2020). A Review on Fault Detection and Process Diagnostics in Industrial Processes. Processes, 8.","DOI":"10.3390\/pr8091123"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.chemolab.2018.10.010","article-title":"Nonlinear fault detection of batch processes based on functional kernel locality preserving projections","volume":"183","author":"He","year":"2018","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_16","unstructured":"Wang, H., Osen, O.L., Li, G., Li, W., Dai, H.N., and Zeng, W. (2015, January 1\u20134). Big data and industrial Internet of Things for the maritime in-dustry in Northwestern Norway. Proceedings of the IEEE Region 10 Annual International Conference TENCON, Macao, China."},{"key":"ref_17","first-page":"136","article-title":"Machine learning approaches for improving condi-tion-based maintenance of naval propulsion plants","volume":"230","author":"Coraddu","year":"2016","journal-title":"Proc. Inst. Mech. Eng. Part M J. Eng. Marit. Environ."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"107968","DOI":"10.1016\/j.oceaneng.2020.107968","article-title":"Machine learning and data-driven fault detection for ship systems operations","volume":"216","author":"Cheliotis","year":"2020","journal-title":"Ocean Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.engappai.2016.10.015","article-title":"Fault diagnosis of marine 4-stroke diesel engines using a one-vs-one extreme learning ensemble","volume":"57","author":"Kowalski","year":"2017","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1134\/S1061830908090064","article-title":"Nondestructive testing of the state of a ship\u2019s hull with an underwater robot","volume":"44","author":"Akinfiev","year":"2008","journal-title":"Russ. J. Nondestruct. Test."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.ress.2018.04.015","article-title":"Condition-based maintenance of naval propulsion systems: Data analysis with minimal feedback","volume":"177","author":"Cipollini","year":"2018","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105539","DOI":"10.1016\/j.ast.2019.105539","article-title":"Kernel extreme learning machine with iterative picking scheme for failure diagnosis of a turbofan engine","volume":"96","author":"Lu","year":"2019","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.cogsys.2018.03.002","article-title":"Rolling element bearing fault diagnosis using convolutional neural network and vibration image","volume":"53","author":"Hoang","year":"2019","journal-title":"Cogn. Syst. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.isatra.2018.07.033","article-title":"Hybrid algorithmic approach oriented to incipient rotor fault diagnosis on induction motors","volume":"80","year":"2018","journal-title":"ISA Trans."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.eswa.2015.09.043","article-title":"Intelligent fault diagnosis of synchronous generators","volume":"45","author":"Gopinath","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100817","DOI":"10.1016\/j.est.2019.100817","article-title":"A deep learning method for online capacity estimation of lithi-um-ion batteries","volume":"25","author":"Shen","year":"2019","journal-title":"J. Energy Storage"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s41688-018-0025-2","article-title":"Unsupervised Deep Learning for Induction Motor Bearings Monitoring","volume":"3","author":"Cipollini","year":"2019","journal-title":"Data-Enabled Discov. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106063","DOI":"10.1016\/j.oceaneng.2019.05.045","article-title":"Data-driven ship digital twin for estimating the speed loss caused by the marine fouling","volume":"186","author":"Coraddu","year":"2019","journal-title":"Ocean Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Theodoropoulos, P., Spandonidis, C.C., Themelis, N., Giordamlis, C., and Fassois, S. (2021). Evaluation of Different Deep-Learning Models for the Prediction of a Ship\u2019s Propulsion Power. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9020116"},{"key":"ref_31","unstructured":"Spandonidis, C., Theodoropoulos, P., and Giordamlis, C. (2020, January 8\u20139). Combined multi-layered big data and responsible AI techniques for en-hanced decision support in Shipping. Proceedings of the International Conference on Decision Aid Sciences and Applica-tions 2020 (DASA20), Sakheer, Bahrain."},{"key":"ref_32","unstructured":"Spandonidis, C., and Giordamlis, C. (2018, January 22\u201326). Data-centric Operations in Oil & Gas Industry by the Use of 5G Mobile Networks and Indus-trial Internet of Things (IIoT). Proceedings of the Thirteenth International Conference on Digital Telecommunications 2018 (ICDT 2018), Athens, Greece."},{"key":"ref_33","unstructured":"Theodoropoulos, P. (2021). Assessment of Novel Deep Learning Methods for Sea Vessel Condition Monitoring. [Undergraduate Thesis, University of Patras]."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/16\/5658\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:49:26Z","timestamp":1760165366000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/16\/5658"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,23]]},"references-count":33,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["s21165658"],"URL":"https:\/\/doi.org\/10.3390\/s21165658","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,23]]}}}