School of Transportation and Logistics Engineering, Wuhan University of Technology, 1040 Heping Avenue, Wuhan, Hubei 430063, PR China; Intelligent Transportation Systems Research Center, Wuhan University of Technology, 1040 Heping Avenue, Wuhan, Hubei 430063, PR China.
School of Transportation and Logistics Engineering, Wuhan University of Technology, 1040 Heping Avenue, Wuhan, Hubei 430063, PR China; State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, 1040 Heping Avenue, Wuhan, Hubei 430063, PR China; National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, 1040 Heping Avenue, Wuhan, Hubei 430063, PR China.
Accid Anal Prev. 2024 Jan;194:107342. doi: 10.1016/j.aap.2023.107342. Epub 2023 Oct 21.
With their complex structure, multiple failure modes and lack of maintenance crew, the safety problem of Maritime Autonomous Surface Ships' (MASS) machinery systems are becoming an important research topic. The present study presents an availability model for ship machinery systems incorporating a maintenance strategy based on Dynamic Bayesian Networks (DBN). First, the availability of conventional ship machinery systems is evaluated and used as a benchmark based on the configuration and planned maintenance strategy. Secondly, the availability of MASS machinery systems is compared to the benchmark, before the introduction of any changes to the ship's configuration and planned maintenance strategy. Finally, the availability improvement strategies, including redundant designs and planned maintenance strategies at port, are proposed based on sensitivity analysis and planned maintenance cost minimization. To exemplify the model's application, a case study of a cooling water system is explored. Based on a sensitivity analysis using the model, it is possible to decide which components need to be redundant. Different redundancy designs and corresponding planned maintenance strategies can be adopted to meet the availability demand. It is also shown that redundancy and enhanced detection capabilities reduce much of the planned maintenance cost. This framework can be used in the early design stages to determine whether the MASS machinery systems' availability is at least equivalent to that of conventional ships, and has certain reference significance for redundant configuration designs and MASS planned maintenance strategy schedule.
由于其复杂的结构、多种失效模式以及缺乏维护人员，海上自主水面船舶（MASS）机械系统的安全性问题正成为一个重要的研究课题。本研究提出了一种基于动态贝叶斯网络（DBN）的船舶机械系统可用性模型，其中包含了一种维护策略。首先，根据配置和计划维护策略，评估常规船舶机械系统的可用性，并将其用作基准。其次，在引入任何船舶配置和计划维护策略的更改之前，将 MASS 机械系统的可用性与基准进行比较。最后，根据敏感性分析和计划维护成本最小化，提出了包括冗余设计和港口计划维护策略在内的可用性改进策略。为了举例说明模型的应用，探讨了冷却水泵系统的案例研究。基于模型的敏感性分析，可以确定需要冗余的部件。可以采用不同的冗余设计和相应的计划维护策略来满足可用性需求。还表明，冗余和增强的检测能力降低了大量的计划维护成本。该框架可用于早期设计阶段，以确定 MASS 机械系统的可用性是否至少等同于传统船舶的可用性，并且对冗余配置设计和 MASS 计划维护策略的制定具有一定的参考意义。
