在露天采矿中预测卡车故障时间的方法基于指数级光滑神经网络
Wei Liu1, Jiayang Sun2, Jinbiao Huang3,4
1College of Science, Liaoning Technical University, Fuxin, China. liuwei@lntu.edu.cn.
Scientific reports
|October 31, 2023
概括
预测开采矿的运输卡车故障对于经济效益至关重要. 一种新的非线性指数平滑方法 (ESNN) 提高了故障时间预测的准确性,有助于维护规划.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
- 预测性维护是指预测性维护.
背景情况:
- 运输卡车在露天采矿业务中至关重要.
- 准确的故障时间预测对于优化矿山经济和运营效率至关重要.
- 像指数式平滑这样的现有方法可能会有显著的预测错误.
研究的目的:
- 调查用于卡车故障时间的传统指数平滑预测错误的原因.
- 引入一种新的非线性指数平滑方法 (ESNN) 以提高故障时间预测.
- 建立拟议ESNN和神经网络结构之间的连接.
主要方法:
- 在指数级光滑方法中分析预测错误.
- 开发非线性指数平滑方法 (ESNN).
- 证明ESNN与神经网络架构的等价性.
- 关于使用增强拉格朗奇函数的ESNN解决方法的建议.
主要成果:
- 与现有技术相比,拟议的ESNN方法显著减少了预测错误.
- 在真实世界数据集上的实验验证证证了ESNN的有效性.
- 通过ESNN方法,可以更准确地预测运输卡车故障时间.
结论:
- ESNN提供了一个更可靠的工具,用于预测露天采矿中的卡车故障时间.
- 这有助于采取主动的维护策略,减少停机时间并改善经济结果.
- 该方法为开发矿山设备强有力的预防性维护计划提供了基础.
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