Prognostic health management of repairable ship systems through different autonomy degree; from current condition to fully autonomous ship

Ahmad BahooToroody*, Mohammad Mahdi Abaei, Osiris Valdez Banda, Pentti Kujala, Filippo De Carlo, Rouzbeh Abbassi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)
64 Downloads (Pure)


Maritime characteristics make the progress of automatic operations in ships slow, especially compared to other means of transportation. This caused a great progressive deal of attention for Autonomy Degree (AD) of ships by research centers where the aims are to create a well-structured roadmap through the phased functional maturation approach to autonomous operation. Application of Maritime Autonomous Surface Ship (MASS) requires industries and authorities to think about the trustworthiness of autonomous operation regardless of crew availability on board the ship. Accordingly, this paper aims to prognose the health state of the conventional ships, assuming that it gets through higher ADs. To this end, a comprehensive and structured Hierarchal Bayesian Inference (HBI)-based reliability framework using a machine learning application is proposed. A machinery plant operated in a merchant ship is selected as a case study to indicate the advantages of the developed methodology. Correspondingly, the given main engine in this study can operate for 3, 17, and 47 weeks without human intervention if the ship approaches the autonomy degree of four, three, and two, respectively. Given the deterioration ratio defined in this study, the acceptable transitions from different ADs are specified. The aggregated framework of this study can aid the researchers in gaining online knowledge on safe operational time and Remaining Useful Lifetime (RUL) of the conventional ship while the system is being left unattended with different degrees of autonomy.
Original languageEnglish
Article number108355
Pages (from-to)1-16
Number of pages16
JournalReliability Engineering and System Safety
Publication statusPublished - May 2022

Bibliographical note

Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • Mass
  • Prognostic health management
  • Remaining useful lifetime
  • Bayesian inference


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