ABSTRACT
BACKGROUND: Maintenance operations on-board ships are highly demanding. Maintenance operations are intensive activities requiring high manemachine interactions in challenging and evolving conditions. The evolving conditions are weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress. For example, extreme weather condition affects seafarers' performance, increasing the chances of error, and, consequently, can cause injuries or fatalities to personnel. An effective human error probability model is required to better manage maintenance on-board ships. The developed model would assist in developing and maintaining effective risk management protocols. Thus, the objective of this study is to develop a human error probability model considering various internal and external factors affecting seafarers' performance. METHODS: The human error probability model is developed using probability theory applied to Bayesian network. The model is tested using the data received through the developed questionnaire survey of >200 experienced seafarers with >5 years of experience. The model developed in this study is used to find out the reliability of human performance on particular maintenance activities. RESULTS: The developed methodology is tested on the maintenance of marine engine's cooling water pump for engine department and anchor windlass for deck department. In the considered case studies, human error probabilities are estimated in various scenarios and the results are compared between the scenarios and the different seafarer categories. The results of the case studies for both departments are also compared. CONCLUSION: The developed model is effective in assessing human error probabilities. These probabilities would get dynamically updated as and when newinformation is available on changes in either internal (i.e., training, experience, and fatigue) or external (i.e., environmental and operational conditions such asweather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress) factors.