Machine learning algorithms in shipping: improving engine fault detection and diagnosis via ensemble methods

  • G. TsaganosEmail author
  • N. Nikitakos
  • D. Dalaklis
  • A.I. Ölcer
  • D. Papachristos


Detection and diagnosis of marine engines faults are extremely important functions for the optimized voyage of any sea-going vessel, as well as the safe conduct of navigation. Early detection of these faults is a prerequisite for reliability: incidents of engine breakdowns can be avoided, since the timely resolving of these faults can ensure the non-interrupted tempo of the sail. Avoiding malfunctions could also improve the ship’s overall environmental “footprint” and even ensure reduced fuel consumption. Initial results of the analysis at hand were presented during the 3rd International Symposium on Naval Architecture and Maritime (INT-NAM 2018), in Istanbul-Turkey. Further exploring the use of machine learning algorithms in shipping and by elaborating more on that effort, an evaluation of intelligent diagnostic methods applicable for a two-stroke slow-speed marine diesel engine is taking place, with the aim to facilitate effective detection and classification of occurring faults. This research was carried out via the cost-free Weka data mining tool, which was used to analyze the data of the engine’s operating parameters that were found outside of the prescribed boundaries. The proposed method is based on the construction of an ensemble classification model “AdaBoost”, which further improves the performance of a basic Simple Cart classifier. During the related experimental activities, the overall recorded performance was 96.5%, clearly establishing this method as a very appropriate choice.


Marine diesel engine Fault detection Machine learning algorithms Ensemble methods Weka Cross-validation Confusion matrix 



  1. Amozegar M, Amozegar M (2015) Aircraft Jet Engine Health Monitoring Through System Identification Using Ensemble Neural Networks. Retrieved from
  2. Ayubi Rad MA, Yazdanpanah MJ (2015) Designing supervised local neural network classifiers based on EM clustering for fault diagnosis of Tennessee Eastman process. Chemom Intell Lab Syst 146:149–157. CrossRefGoogle Scholar
  3. Dalaklis D (2018) Exploring the issue of technology trends in the “era of digitalisation”, World Maritime Day Parallel Event, Szczecin-Poland,13 June 2018. DOI:
  4. Dalaklis D, Baldauf M, Kitada MM (2018) Vulnerabilities of the Automatic Identification System in the Era of Maritime Autonomous Surface Ships, 9th NMIOTC Annual Conference (Fostering Projection of Stability through Maritime Security: Achieving Enhanced Capabilities and Operational Effectiveness), Chania-Greece, 7 June 2018. DOI:
  5. Engine Selection Guide Two-stroke MC/MC-C Engines. (2000) (5th ed.). Retrieved from
  6. Hu J,Xie S, Kailong C, He Xiuran PJ (2007) Classification Method of DivClassification Method of Diverse AdaBoost-SVM and Its Application to Fault Diagnosis of Aeroengine. Retrieved from
  7. Kitada M, Baldauf M, Mannov A, Svendsen PAS, Baumler R, Schröder-Hinrichs J-U, Dalaklis D, Fonseca T, Shi X, Lagdami K (2018) Command of vessels in the era of digitalization. In: Kantola J, Nazir S, Barath T (eds) Advances in human factors, business management and society. AHFE 2018. Advances in intelligent systems and computing, vol 783. Springer, Cham, pp 339–350Google Scholar
  8. Kyrtatos N (2015) Marine Diesel Engines, ISBN: 978-960-266-002-7, Publisher by Symmetria, Address Ioannou Theologos 80 Zografou, Athens, Greece, (in Greek)Google Scholar
  9. Lan W-C, Katagi T, Hashimoto T (1996) Quasi Steady State Simulation of Diesel Engine Transient Performance and Design of Mechatronic Governor*. Retrieved from
  10. Lary DJ, Alavi AH, Gandomi AH, Walker AL (2016) Machine learning in geosciences and remote sensing. Geoscience Frontiers 7 (1):3–10CrossRefGoogle Scholar
  11. Lazarou X Kliani, Ioannis K, Nikalou IA (2003) Internal Combustion Engines Volume Two, (in Greek)Google Scholar
  12. Li Z, Yan X, Guo Z, Zhang Y, Yuan C, Peng Z (2012) Condition monitoring and fault diagnosis for marine diesel engines using information fusion techniques. Electronics and Electrical Engineering 123(7):109–112. CrossRefGoogle Scholar
  13. Margaronis I.E (1986) Function diagnostic Systems in Marine Diesel Engines. Doctoral thesis. The National Technical University of Athens, (in Greek)Google Scholar
  14. Mitchell TM (2006) The Discipline of Machine Learning, (July). Retrieved from
  15. Mohammed, M., Khan, M., Bashier, E. (2017). Machine Learning. Boca Raton: CRC Press,
  16. Nikitakos N, Dalaklis D, Siousiouras P (2018) Real time awareness for MRV data. In: Ölçer AI, Kitada M, Dalaklis D, Ballini F (eds) Trends and challenges in maritime energy management, Springer, Cham ISBN 978-3-319-74576-3CrossRefGoogle Scholar
  17. Rokach L (2010) Ensemble-based classifiers. Artif Intell Rev 33(1–2):1–39. CrossRefGoogle Scholar
  18. Ross KA, Jensen CS, Snodgrass R, Dyreson CE, Jensen CS, Snodgrass R et al (2009) Cross-validation. In: Encyclopedia of database systems. Springer US, Boston, pp 532–538. CrossRefGoogle Scholar
  19. Sahin F, Yavuz MÇ, Arnavut Z, Uluyol Ö (2007) Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization. Parallel Comput 33(2):124–143. CrossRefGoogle Scholar
  20. Schapire R (2013) Theoretical machine learning, 1–7Google Scholar
  21. Sharkey AJC, Chandroth GO, Sharkey NE (2000) A multi-net system for the fault diagnosis of a diesel engine. Neural Comput Applic 9(2):152–160. CrossRefGoogle Scholar
  22. Singh Sabharwal J (n.d.) Multi-Label Text Classification. Retrieved from
  23. Skountrianos H (2005) Modeling and diagnosis-recognition of non-linear dynamic systems faults with neural networks of local models. Doctoral thesis. The National Technical University of Athens, (in Greek)Google Scholar
  24. Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45:427–437. CrossRefGoogle Scholar
  25. Tselenti GN (1998) Fault diagnosis through vibration control in industrial line laundry production. Doctoral thesis. The Technical University of Crete, (in Greek)Google Scholar
  26. Twiddle JA, Jones NB (2002) A high-level technique for diesel engine combustion system condition monitoring and fault diagnosis. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 216(2):125–134. CrossRefGoogle Scholar
  27. User’s Guide PMI System (2005) In MAN B&W Diesel A/S (2.3, p. 82). Retrieved from
  28. Wong PK, Zhong J, Yang Z, Vong CM (2016) Sparse Bayesian extreme learning committee machine for engine simultaneous fault diagnosis. Neurocomputing 174:331–343. CrossRefGoogle Scholar
  29. Xiros NI, Kyrtatos NP (2000) A neural predictor of propeller load demand for improved control of diesel ship propulsion. In Proceedings of the 2000 IEEE International Symposium on Intelligent Control. Held jointly with the 8th IEEE Mediterranean Conference on Control and Automation (Cat. No.00CH37147) (pp. 321–326). IEEE.

Copyright information

© World Maritime University 2020

Authors and Affiliations

  • G. Tsaganos
    • 1
    Email author
  • N. Nikitakos
    • 2
  • D. Dalaklis
    • 3
  • A.I. Ölcer
    • 3
  • D. Papachristos
    • 4
  1. 1.Department of Marine EngineeringMerchant Academy of AthensAthensGreece
  2. 2.Department of Shipping Trade & TransportChiosGreece
  3. 3.World Maritime University (WMU)MalmoSweden
  4. 4.Department of Industrial Engineering and ProductionChiosGreece

Personalised recommendations