Islam Rabiul, Khan Faisal, Abbassi Rouzbeh, Garaniya Vikram
National Centre for Maritime Engineering and Hydrodynamics (NCMEH), Australian Maritime College (AMC), University of Tasmania, Launceston, Australia.
Centre for Risk, Integrity and Safety Engineering (C-RISE), Process Engineering Department, Memorial University of Newfoundland, St. John's, NL, Canada.
Saf Health Work. 2018 Mar;9(1):42-52. doi: 10.1016/j.shaw.2017.06.008. Epub 2017 Jun 28.
Maintenance operations on-board ships are highly demanding. Maintenance operations are intensive activities requiring high man-machine 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.
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.
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.
The developed model is effective in assessing human error probabilities. These probabilities would get dynamically updated as and when new information is available on changes in either internal (i.e., training, experience, and fatigue) or external (i.e., environmental and operational conditions such as weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress) factors.
船舶上的维护操作要求极高。维护操作是高强度活动，在具有挑战性且不断变化的条件下需要高度的人机交互。这些不断变化的条件包括天气状况、工作场所温度、船舶运动、噪音和振动，以及工作量和压力。例如，极端天气状况会影响海员的表现，增加出错几率，进而可能导致人员受伤或死亡。需要一个有效的人为失误概率模型来更好地管理船舶上的维护工作。所开发的模型将有助于制定和维护有效的风险管理协议。因此，本研究的目的是开发一个考虑影响海员表现的各种内部和外部因素的人为失误概率模型。
使用应用于贝叶斯网络的概率论开发人为失误概率模型。该模型通过对200多名有超过5年经验的经验丰富海员进行的问卷调查所获得的数据进行测试。本研究中开发的模型用于找出特定维护活动中人员表现的可靠性。
所开发的方法在轮机部的船舶主机冷却水泵维护和甲板部的起锚机维护上进行了测试。在所考虑的案例研究中，估计了各种场景下的人为失误概率，并在不同场景和不同海员类别之间比较了结果。还比较了两个部门的案例研究结果。
所开发的模型在评估人为失误概率方面是有效的。当有关于内部（即培训、经验和疲劳）或外部（即环境和操作条件，如天气状况、工作场所温度、船舶运动、噪音和振动，以及工作量和压力）因素变化的新信息时，这些概率将动态更新。
