A Multi-agent-based Simulation Method for Health State Assessments of Naval Equipment

被引：0
|
作者：
Ding S. [1 ,2 ]
Wang M. [1 ,2 ]
Dong Z. [1 ,2 ]
Nie L. [1 ,2 ]
机构：
[1] School of Mechanical Engineering, Hubei University of Technology, Wuhan
[2] Hubei Key Laboratory of Manufacturing Quality Engineering, Wuhan
来源：
Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2022年 / 33卷 / 10期
关键词：
Health assessment; Multi-agent system; Naval equipment; Sea condition;
D O I：
10.3969/j.issn.1004-132X.2022.10.005
中图分类号：
学科分类号：
摘要：
Considering that the sea conditions had a significant impact on the safe navigation and mission execution of ships, a multi-agent technology-based equipment health state simulation assessment method was proposed herein. Based on the analysis of naval equipment navigation task, the "task-equipment-maintenance-environment" equipment health state model was established, and the internal rules and communication mechanism of the environmental agents were formulated to realize the health state assessment of naval equipment under different sea conditions, in view of the influences of sea condition factors on equipment performance and maintenance capability. Taking a ship's power system as the object, the health states of the cruising mission was evaluated under sea state 0, 5 and 7 level, respectively, and the simulation analysis results show that compared with sea state 0, the health and mission success rate of the power system under sea state 5 and 7 are reduced, and the quantitative evaluation results are consistent with the trend of expert qualitative evaluation results. © 2022, China Mechanical Engineering Magazine Office. All right reserved.
引用
收藏
页码：1169 / 1177
页数：8
相关论文
共 19 条
  • [1] MENG Qinghai, CHEN Yang, Research on Integrated Equipment Assurance Work System and Collaborative Design Environment Based on Mission Success Rate, Environmental Technology, 32, 1, pp. 27-29, (2014)
  • [2] SHE J, YAO W, SHI J, Et al., Multi-dimensional Health Evaluation of Equipment in Electric Power Communication Network[C], IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference(IMCEC), pp. 40-43, (2019)
  • [3] ATAMURADOV V, MEDJAHER K, CAMCI F, Et al., Railway Point Machine Prognostics Based on Feature Fusion and Health State Assessment[J], IEEE Transactions on Instrumentation and Measurement, 68, 8, pp. 2691-2704, (2018)
  • [4] YIN Guodong, LI Guangming, ZHU Dong, Et al., Research on Multi-Agent Energy Management System for Four-wheel Drive Electric Vehicles, China Mechanical Engineering, 29, 15, pp. 1765-1771, (2018)
  • [5] BOES J, MIGEON F., Self-organizing Multi-agent Systems for the Control of Complex Systems, Journal of Systems and Software, 134, pp. 12-28, (2017)
  • [6] GHADIMI P, TOOSI F G, HEAVEY C., A Multi-Agent Systems Approach for Sustainable Supplier Selection and Order Allocation in a Partnership Supply Chain[J], European Journal of Operational Research, 269, 1, pp. 286-301, (2018)
  • [7] DING Shanting, WANG Miao, DONG Zheng-qiong, Et al., A Multi-intelligence-based Assessment Method for Shipboard Radar Reliability, Maintainability and Assurance, China Ship Research, 16, 6, pp. 52-60, (2021)
  • [8] FENG Q, HAI X, HUANG B, Et al., An Agent-based Reliability and Performance Modeling Approach for Multistate Complex Human-machine Systems with Dynamic Behavior[J], IEEE Access, 7, pp. 135300-135301, (2019)
  • [9] KOU Li, FAN Wenhui, SONG Shuang, Et al., Modeling and Simulation of Equipment Assurance System Based on Multi-intelligence Body, China Science:Information Science, 48, 7, pp. 794-809, (2018)
  • [10] RAJPUT P K, SIKKA G., Multi-agent Architecture for Fault Recovery in Self-healing Systems, Journal of Ambient Intelligence and Humanized Computing, 12, pp. 2849-2866, (2021)
12