一个混合Bi-LSTM模型用于数据驱动的维护规划
Alexandros Noussis1, Ryan O'Neil1, Ahmed Saif1
1Department of Industrial Engineering, Dalhousie University, Halifax, NS Canada.
概括
本研究介绍了一种混合深度学习模型,用于优化资产维护. 该框架生成剩余使用寿命 (RUL) 预测,以改进选择性维护规划并降低复杂工业系统的成本.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 运营研究 运营研究
背景情况:
- 现代工业需要在资源限制下高效地维护资产.
- 传统的维护方法由于估计错误和计算复杂性而面临限制.
- 工业4.0和深度学习 (DL) 能够为维护计划提供数据驱动的健康预测.
研究的目的:
- 为了弥合基于DL的剩余使用寿命 (RUL) 预测和维护计划优化之间的差距.
- 为选择性维护问题 (SMP) 开发一个可扩展和准确的框架.
- 为了优化面向任务的系列k-out-of-n:G系统的维护.
主要方法:
- 开发一种混合DL模型,将蒙特卡罗脱落纳入RUL预测中.
- 从RUL预测中构建实证系统可靠性函数.
- 使用生成的可靠性函数优化选择性维护问题 (SMP).
主要成果:
- 拟议的框架有效地优化了维护计划,尽量减少成本,同时确保任务的生存.
- 数字实验表明,与之前的SMP方法相比,其性能优越.
- 该方法在没有计算密集的参数可靠性函数的情况下提供精确的解决方案.
结论:
- 开发的混合DL框架为复杂的工业维护场景提供了可扩展和准确的解决方案.
- 它可以实现数据驱动,优化维护规划,提高运营效率和降低成本.
- 该方法适用于各种工业环境和系统配置.
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