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对GWO和J-SLnO进行协调整合,以在工业4.0中优化资产管理和预测性维护
A N Arularasan1, P Ganeshkumar2, Mohammad Alkhatib2
1Department of Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai 600 048, Tamil Nadu, India.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
先进的优化算法,灰狼优化 (GWO) 和基于Jaya的海狮优化 (J-SLnO),显著提高了工业4.0资产管理和预测性维护效率和成本效益.
科学领域:
- 工业工程 工业工程 工业工程
- 计算机科学 计算机科学
- 运营研究 运营研究
背景情况:
- 工业4.0需要资产管理和预测性维护的先进解决方案.
- 传统的调度算法 (MinMin,MaxMin,FCFS,Round Robin) 在效率和成本效益方面往往不足.
- 优化算法为增强的工业流程提供了潜力.
研究的目的:
- 应用和评估灰狼优化 (GWO) 和基于Jaya的海狮优化 (J-SLnO) 在工业4.0.0中用于资产管理和预测性维护.
- 为了比较GWO和J-SLnO的性能与传统的调度算法.
- 评估这些算法对效率,成本和能源消耗的影响.
主要方法:
- 灰狼优化 (GWO) 和基于Jaya的海狮优化 (J-SLnO) 的实施.
- 这些算法与资源调度技术的整合.
- 与标准算法进行比较分析:MinMin,MaxMin,FCFS和Round Robin.
主要成果:
- GWO将执行时间减少了13-31%;J-SLnO减少了16-33%.
- GWO提供了8.57-9.17%的成本节约;J-SLnO实现了13.56-19.71%的节约.
- 这两种算法都显示出显著的能效,能耗降低了94.1-94.2%.
- 在预测建模中,J-SLnO显示出卓越的准确性和稳定性.
结论:
- 对于工业4.0资产管理,GWO和J-SLnO在效率,成本效益和能源消耗方面取得了重大改进.
- 对于准确可靠的预测性维护应用,J-SLnO特别有效.
- 该研究验证了这些先进的优化技术的实际工业实施,为更智能的操作铺平了道路.
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