Fault Prediction and Condition-Based Maintenance of Marine Engines

Francesco Maione; Paolo Lino; Giuseppe Giannino; Erlend M. Coates; Ottar L. Osen; Guido Maione

Abstract

The development and implementation of machine learning and deep learning algorithms has opened up new possibilities in industry. Maintenance of industrial assets has received wide interest from scientists and practitioners. Condition-based maintenance (CBM) has proven to be necessary to reduce downtime and the cost of repairs and inspections, and to predict and prevent faults. In the maritime industry, marine engines are complex systems that must work in a rough environment; therefore, implementing safe, reliable, and efficient maintenance strategies is challenging. In this context, artificial intelligence (AI) algorithms have shown high reliability and versatility with respect to traditional model-based and knowledge-based techniques. In this work, we propose an unified framework explaining the implementation of suitable AI algorithms to predict the main faults affecting the performance of a marine engine. By an accurate analysis, aging and catastrophic faults are the most common ones. Then, we describe strategies to detect these two types of faults. We also describe the requirements that a decision support system should satisfy to integrate the algorithms for applying an innovative CBM strategy into real-life scenarios. Finally, we present the challenges and provide some suggestions for future research. In summary, the main finding of this work is the framework integrating AI-based efficient strategies for predicting aging and catastrophic faults with significant advance. In detail, prediction of catastrophic faults gains nearly 30 seconds with respect to a classical prediction method. These findings enable us to effectively implement CBM of marine engines considered in this research.

References

1.
Angelopoulos, A.; Giannopoulos, A.; Nomikos, N.; et al. Federated Learning-Aided Prognostics in the Shipping 4.0: Principles, Workflow, and Use Cases. IEEE Access 2024, 12, 6437–6454.
2.
Lazakis, I.; Olc¸er, A. Selection of the best maintenance approach in the maritime industry under fuzzy multiple attributive group decision making environment. Proc. Inst. Mech. Eng. Part M: J. Eng. Marit. Environ. 2015, 230, 297–309.
3.
Industry Analytics Research Consulting. Marine Engine Market—Forecast (2026–2032), Report Code: EPR 0004. Available online: https://www.industryarc.com/Report/15099/marine-engine-market.html#: ˜ :text=Furthermore%
2C%20downtime%20due%20to%20engine,of%20the%20marine%20engines%20market (accessed on 31 March 2026).
4.
Molland, A.F. Chapter 11—Marine Safety. In The Maritime Engineering Reference Book—A Guide to Ship Design, Construction and Operation; Butterworth-Heinemann: Oxford, UK, 2008; 784–875.
5.
Han, P.; Ellefsen, A.L.; Li, G.; et al. Fault Prognostics Using LSTM Networks: Application to Marine Diesel Engine. IEEE Sens. J. 2021, 21, 25986–25994.
6.
Ellefsen, A.L.; Bjøorlykhaug, E.; Æsøy, V.; et al. An Unsupervised Reconstruction-Based Fault Detection Algorithm for Maritime Components. IEEE Access 2019, 7, 16101–16109.
7.
Maione, F.; Lino, P.; Maione, G.; et al. A Machine Learning Framework for Condition-Based Maintenance of Marine Diesel Engines: A Case Study. Algorithms 2024, 17, 411.
8.
Susto, G.A.; Schirru, A.; Pampuri, S.; et al. Machine Learning for Predictive Maintenance: A Multiple Classifiers Approach. IEEE Trans. Ind. Informat. 2015, 11, 812–820.
9.
Maione, F.; Lino, P.; Maione, G.; et al. Predictive Maintenance of Marine Engines using a Long Short-Term Memory Algorithm. In Proceedings of the 2024 IEEE 8th Forum on Research and Technologies for Society and Industry (RTSI), Lecco, Italy, 18–20 September 2024; pp. 578–583.
10.
Langone, R.; Alzate, C.; De Ketelaere, B.; et al. Kernel spectral clustering for predicting maintenance of industrial machines. In Proceedings of the 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Singapore, 16–19 April 2013; pp. 39–45.
11.
Han, P.; Ellefsen, A.L.; Li, G.; et al. Fault Detection with LSTM-Based Variational Autoencoder for Maritime Components. IEEE Sens. J. 2021, 21, 21903–21912.
12.
Cipollini, F.; Oneto, L.; Corradu, A.; et al. Condition-Based Maintenance of Naval Propulsion System with Supervised Data Analysis. Ocean Eng. 2018, 149, 268–278.
13.
Wu, W. Predictive Maintenance: How It Works, Costs & Key Examples. Available online: https://coastapp.com/blog/predictive-maintenance/ (accessed on 27 March 2026).
14.
Liang, Q.; Knutsen, K.E.; Vanem, E.; et al. A review of maritime equipment prognostics health management from a classification society perspective. Ocean Eng. 2024, 301, 117619.
15.
Niazi, M.A. Predictive vs. Preventive Maintenance. Available online: https://control.com/technical-articles/predictive-vspreventive-maintenance/ (accessed on 27 March 2026).
16.
Nithin, S.K.; Hemanth, K.; Shamanth, V. A Review on Combustion and Vibration Condition Monitoring of IC Engine. Mater. Today Proc. 2021, 45, 65–70.
17.
Ventikos, N.P.; Sotiralis, P.; Annetis, E. A combined risk-based and condition monitoring approach: developing a dynamic model for the case of marine engine lubrication. Transp. Saf. Environ. 2022, 4, tdac020.
18.
Awang, M.N.; Zali, Z.; Mohd Noor, N.A.; et al. Main Propulsion Marine Diesel Engine Condition Based Maintenance Monitoring Using Ultrasound Signal. In Advancement in Emerging Technologies and Engineering Applications; Lecture Notes in Mechanical Engineering; Saw, C., Woo, T., Karam Singh, S., et al., Eds.; Springer: Singapore, 2020; pp. 175–187.
19.
Haxhiu, A.; Abdelhakim, A.; Kanerva, S.; et al. Electric Power Integration Schemes of the Hybrid Fuel Cells and Batteries-Fed Marine Vessels—An Overview. IEEE Trans. Transp. Electrific. 2022, 8, 1885–1905.
20.
Pagan Rubio, J.A.; Vera-Garc´ıa, F.; Grau, J.H.; et al. Marine Diesel Engine Failure Simulator Based on Thermodynamic Model. Appl. Therm. Eng. 2018, 144, 982–995.
21.
Boschert, S.; Rosen, R. Digital Twin—The Simulation Aspect. In Mechatronic Futures; Hehenberger, P., Bradley, D., Eds.; Springer: Cham, Switzerland, 2016; pp. 59–74.
22.
Kougiatsos, N.; Vessa, R. A Distributed Cyber-Physical Framework for Sensor Fault Diagnosis of Marine Internal Combustion Engines. IEEE Trans. Control Syst. Technol. 2024, 32, 1718–1729.
23.
Jaramillo Jimenez, V.; Bouhmala, N.; Gausdal, A.H. Developing a predictive maintenance model for vessel machinery. J. Ocean Eng. Sci. 2020, 5, 358–386.
24.
Ali, H.; Safdar, R.; Liu, J.; et al. Hybrid fusion paradigm in advanced process monitoring: a panoramic review and future perspectives. Ind. Eng. Chem. Res. 2025, 64, 22465–22514.
25.
Husnain, A.; Rizwan, S.; Muhammad, H. R.; et al. Advance industrial monitoring of physio-chemical processes using novel integrated machine learning approach. J. Ind. Inf. Integr. 2024, 42, 100709.
26.
Husnain, A.; Rizwan, S.; Jinfeng, L.; et al. Process monitoring and dynamic fusion of complex industrial systems: A reconstruction-based Bayesian framework. Comput. Chem. Eng. 2025, 203, 109352.
27.
Ali, H.; Safdar, R.; Ding, W.; et al. Intelligent machine learning-based multi-model fusion monitoring: Application to industrial physio-chemical systems. Control Eng. Pract. 2025, 162, 106361.
28.
Ali, H.; Gao, F. Fault detection and diagnosis using reconstruction-based DiGLPP: application to industrial distillation system. IFAC-PapersOnLine 2025, 59, 187–192.
29.
Ali, H.; Safdar, R.; Zhou, Y.; et al. A novel dynamic machine learning-based explainable fusion monitoring: application to industrial and chemical processes. Mach. Learn. Sci. Technol. 2025, 6, 015005.
30.
Gharib, H.; Kov´acs, G. Development of a New Expert System for Diagnosing Marine Diesel Engines Based on Real-Time Diagnostic Parameters. J. Mech. Eng. 2022, 68, 642–653.
31.
Karatug, C.; Arslano˘glu, Y.; Guedes Soares, C. Design of Decision Support System to achieve condition-based maintenance in ship machinery systems. Ocean Eng. 2023, 281, 114611.
32.
Kim, Y.; Lee, K.; Han, Y. Exploring large language models (LLMs)-based chatbots for pump maintenance in ships. Ships Offshore Struct. 2025, 1–19. https://doi.org/10.1080/17445302.2025.2550529.
33.
Youssef, A.; Noura, H.; El Amrani, A.; et al. A Survey on Data-Driven Fault Diagnostic Techniques for Marine Diesel Engines. IFAC-PapersOnline 2024, 58, 55–60.
34.
Ellefsen, A.L.; Æsøy, V.; Ushakov, S.; et al. A Comprehensive Survey of Prognostics and Health Management Based on Deep Learning for Autonomous Ships. IEEE Trans. Reliab. 2019, 68, 720–740.
35.
Jennions, I.K.; Niculita, O.; Esperon-Miguez,M. Integrating IVHMand Asset Design. Int. J. Progn. Health Manag. 2016, 7, 1–16.
36.
Rajamani, R.; Saxena, A.; Kramer, F.; et al. Developing IVHM Requirements for Aerospace Systems; SAE Technical Paper 2013-01-2333; SAE: Warrendale, PA, USA, 2013.
37.
Zocco, F.; Wang, H.-C.; Van, M. Digital Twins for Marine Operations: A Brief Review on Their Implementation. arXiv 2023, arXiv.2301.09574.
38.
Menges, D.; Rasheed, A. Digital Twin for Autonomous Surface Vessels: Enabler for Safe Maritime Navigation. arXiv 2024, arXiv:2411.03465.
39.
Pang, T.Y.; Pelaez Restrepo, J.D.; Cheng, C.-T.; et al. Developing a Digital Twin and Digital Thread Framework for an ‘Industry 4.0’ Shipyard. Appl. Sci. 2021, 11, 1097.
40.
Blanke, M.; Frei, W.C.; Kraus, F.; et al. What is Fault-Tolerant Control? IFAC Proc. Vol. 2000, 33, 41–52.
41.
Izadi-Zamanabadi, R.; Blanke, M. A Ship Propulsion System as a Benchmark for Fault-Tolerant Control. Control Eng. Pract. 1999, 7, 227–239.
42.
TC 6.4. Fault Detection, Supervision & Safety of Technical Processes-SAFEPROCESS. Terminology in the Area of Fault Management. Available online: https://tc.ifac-control.org/6/4/terminology/terminology-in-the-area-of-fault-management (accessed on 27 March 2026).
43.
Isermann, R.; Ball´e, P. Trends in the Application of Model-Based Fault Detection and Diagnosis of Technical Processes. Control Eng. Pract. 1997, 5, 709–719.
44.
Isermann, R. Process fault detection based on modeling and estimation methods—A survey. Automatica 1984, 20, 387–404.
45.
Isermann, R. Model-based fault detection and diagnosis—Status and applications. Annu. Rev. Control 2005, 29, 71–85.
46.
Isermann, R. Fault Diagnosis Systems—An Introduction from Fault Detection to Fault Tolerance; Springer Verlag: Berlin/Heidelberg, Germany, 2005.
47.
Patton, R.J. Fault detection and diagnosis in aerospace systems using analytical redundancy. In IEE Colloquium on Condition Monitoring and Fault Tolerance; IET: London, UK, 1990.
48.
Frank, P.M.; Ding, X. Frequency domain approach to optimally robust residual generation and evaluation for model-based fault diagnosis. Automatica 1994, 30, 789–804.
49.
Basseville, M. Information criteria for residual generation and fault detection and isolation. Automatica 1997, 33, 783–803.
50.
Frank, P.M.; Ding, X. Survey of robust residual generation and evaluation methods in observer-based fault detection systems. J. Process Control 1997, 7, 403–424.
51.
Patton, R.J.; Chen, J. Observer-based fault detection and isolation: Robustness and applications. Control Eng. Pract. 1997, 5, 671–682.
52.
Simani, S. Identification and Fault Diagnosis of a Simulated Model of an Industrial Gas Turbine. IEEE Trans. Ind. Informat. 2005, 1, 202–216.
53.
Puig, V.; Stancu, A.; Escobet, T.; et al. Passive robust fault detection using interval observers: Application to the DAMADICS benchmark problem. Control Eng. Pract. 2006, 14, 621–633.
54.
Puig, V.; Quevedo, J.; Escobet, T.; et al. Passive Robust Fault Detection of Dynamic Processes Using Interval Models. IEEE Trans. Control Syst. Technol. 2008, 16, 1083–1089.
55.
Serdio, F.; Lughofer, E.; Pichler, K.; et al. Residual-based fault detection using soft computing techniques for condition monitoring at rolling mills. Inf. Sci. 2014, 259, 304–320.
56.
Simani, S.; Farsoni, S.; Castaldi, P. Fault Diagnosis of a Wind Turbine Benchmark via Identified Fuzzy Models. IEEE Trans. Ind. Electron. 2015, 62, 3775–3782.
57.
Fang, X.; Blesa, J.; Puig, V. Fault Prognosis Approach Using Data-Driven Structurally Generated Residuals. In Proceedings of the 2024 32nd Mediterranean Conference on Control and Automation (MED), Chania, Greece, 1–14 June 2024; pp. 531–536.
58.
Maione, F.; Lino, P.; Giannino, G.; et al. A New Deep Learning Strategy to Predict Time-Series of Marine Diesel Engines. IEEE Trans. Ind. Appl. 2026, 62, 2300–2310.
59.
Ceylan, B.O.; Akyuz, E.; Arslano˘glu, Y. Modified quantitative systems theoretic accident model and processes (STAMP) analysis: A catastrophic ship engine failure case. Ocean Eng. 2022, 253, 111187.
60.
Liu, S.; Shen, C.; Chen, Z.; et al. A sudden fault detection network based on Time-sensitive gated recurrent units for bearings. Measurement 2021, 186, 110214.
61.
Maione, F.; Lino, P.; Maione, G.; et al. A Machine Learning Method to Early Detect Catastrophic Failures of Marine Diesel Engines: A Case Study. In Proceedings of the 2025 European Control Conference (ECC), Thessaloniki, Greece, 24–27 June 2025; pp. 3195–3200.
62.
Dotoli, M.; Lino, P. Fuzzy Adaptive Control of a Variable Geometry Turbocharged Diesel Engine. In Proceedings of the 2002 IEEE International Symposium on Industrial Electronics, L’Aquila, Italy, 8–11 July 2002; Volume 4, pp. 1295–1300.
63.
Bondarenko, O.; Fukuda, T. Development of a Diesel Engine’s Digital Twin for Predicting Propulsion System Dynamics. Energy 2020, 196, 117126.
64.
Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput. 1997, 9, 1735–1780.
65.
Makridis, G.; Kyriazis, D.; Plitsos, S. Predictive maintenance leveraging machine learning for time-series forecasting in the maritime industry. In Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece, 20–23 September 2020; pp. 1–8.
66.
Holland, J.H. Adaptation in Natural and Artificial Systems; The MIT Press: Cambridge, MA, USA, 1992.
67.
Zhuo, Y.; Yin, Z.; Ge, Z. Attack and Defense: Adversarial Security of Data-Driven FDC Systems. IEEE Trans. Ind. Informat. 2023, 19, 5–19.
68.
Giannino, G.; Maione, F.; Lino, P.; et al. Digitalization of Marine Power Sources: From Theory to Practice. In Proceedings of the International Maritime Transport and Logistics Conference “MARLOG 14”, Alexandria, Egypt, 23–25 February 2025; pp. 307–318.
69.
Han, P.; Liu, Z.; Sun, Z.; et al. A novel prediction model for ship fuel consumption considering shipping data privacy: An XGBoost-IGWO-LSTM-based personalized federated learning approach. Ocean Eng. 2024, 302, 117668.
70.
Shrestha, R.; Mohammadi, M.; Sinaei, S.; et al. Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid. J. Parallel Distrib. Comput. 2024, 193, 104951.
71.
Hasan, A.; Kandemir, E.; Mordasov, D.; et al. Agentic AI in Wind Energy Systems: Multi-Agent Architectures for Optimization and Resilience. IEEE Access 2026, 14, 9935–9951.
72.
Zaccaria, V.; Sartor, D.; Del Favero, S.; et al. Fault Identification Enhancement with Reinforcement Learning (FIERL). In 2024 American Control Conference (ACC), Toronto, ON, Canada, 10–12 July 2024; pp. 1494–1499.
73.
Li, Y.; Wang, Y.; Zhao, X.; et al. A deep reinforcement learning-based intelligent fault diagnosis framework for rolling bearings under imbalanced datasets. Control Eng. Pract. 2024, 145, 105845.

Scilight Press

Author Information

Abstract

Keywords

References

About Scilight

Journals

Publishing Policies

Contact Us