基于数字孪生与改进KD树算法的船舶运维知识推理与策略优化

Knowledge reasoning and strategy optimization for ship operation and maintenance based on digital twin and improved KD tree algorithm

    摘要
  • 摘要:
    目的 随着工业技术的持续发展，现代船舶智能化进程持续推进，船舶的推进系统、辅助动力系统等变得越发智能化，船舶维护工作变得越发复杂。与陆地设备不同，船舶所处的环境更加恶劣，一旦出现问题，不但会对船舶运行时的稳定性造成影响，还有巨大的安全隐患。
    方法 本文重点研究基于数字孪生的船舶运维知识推理方法，在船舶物理实体的基础上，分析船舶运维过程，从“几何−物理−行为−规则”多维度构建船舶运维数字孪生模型。针对船舶运维知识模型中出现的预警信息，利用以往船舶运维案例，建立包含船舶运行状态数据以及船舶维护方法的船舶运维案例库。基于船舶运维案例库，提出一种改进型KD树算法的船舶运维知识推理与策略生成方法，利用高斯距离加权对邻近案例加权，并以知识推理的准确性为目标，使用鲸鱼优化算法（WOA）对船舶设备特征属性进行优化。
    结果 实验结果表明，提出的改进型KD树算法（ω-KDtree-WOA）在K值为4、种群数为400的情况下，其推理准确性达到0.928，比传统的KD树算法在同条件下提升约3.2%。此外，与基于类置信加权与距离加权的K-近邻算法（CCW-WKNN）和平滑权距离求解K-近邻算法（SDWKNN）等相比，本文所提算法在准确率、召回率、精确率和F1分数上均有显著优势，尤其在K值较大时，表现出更强的稳定性。
    结论 所提方法能有效适用于船舶燃气轮机运维过程。

     

    Abstract:
    Objective  With the continuous development of industrial technology, the intelligence of modern ship processes has been continuously advancing. The propulsion system, auxiliary power system, etc. of ships have become increasingly intelligent, and ship maintenance work has become ever more complex. Different from land equipment, the environment in which ships are located is more severe. When a problem occurs, it will not only affect the stability of the ship during operation, but also bring huge safety hazards.
    Method This paper focuses on a knowledge reasoning method for ship operation and maintenance based on digital twin technology. Based on the physical entity of the ship, the ship operation and maintenance process is analyzed, and a digital twin model for ship operation and maintenance is constructed from the multi-dimensions of "geometry-physics-behavior-rule". Aiming at the early warning information in the ship operation and maintenance knowledge model, by using previous ship operation and maintenance cases, a ship operation and maintenance case database containing ship dynamic monitoring data and maintenance methods is established. Based on the database, a method for ship operation and maintenance knowledge reasoning and strategy generation using an improved KD tree algorithm is proposed. Neighboring cases are weighted using Gaussian distance weighting, and the whale optimization algorithm (WOA) is used to optimize the characteristic attributes of ship equipment to achieve accurate knowledge reasoning.
    Results The experimental results show that the proposed improved KD tree algorithm (ω-KDtree-WOA) achieves an inference accuracy of 0.928 when the K value is 4 and the population size is 400, which is approximately 3.2% higher than that of the traditional KD tree algorithm under the same conditions. In addition, compared with the classification confidence weighted and distance weighted K-nearest neighbor algorithm (CCW-WKNN) and the smoothing weight distance to solve K-nearest neighbor (SDWKNN) algorithm, etc., the algorithm proposed in this paper has significant advantages in accuracy, recall, precision, and F1 score, especially showing stronger stability when the K value is larger.
    Conclusion The proposed method can be effectively applied to the operation and maintenance process of ship gas turbines.

     

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