数字孪生与深度学习融合驱动的采煤机健康状态预测

被引：49

作者：

丁华 ^{[1
,2
]}

杨亮亮 ^{[1
,2
]}

杨兆建 ^{[1
,2
]}

王义亮 ^{[1
,2
]}

机构：

[1] 太原理工大学机械与运载工程学院

[2] 煤矿综采装备山西省重点实验室

来源：

中国机械工程 | 2020年 / 31卷 / 07期

关键词：

数字孪生; 深度学习; 采煤机; 健康预测; 剩余寿命预测;

D O I：

暂无

中图分类号：

TD421.6 [];

学科分类号：

0819 ;

摘要：

针对处于恶劣工作环境的采煤机状态预测与维护困难的问题,结合数字孪生高逼真度行为仿真特性和深度学习强大的数据挖掘能力,提出数字孪生与深度学习融合驱动的采煤机健康状态预测方法。基于物理空间多物理参数构建采煤机数字孪生体,通过在虚拟空间的可视化展示与分析实现健康状态预判;建立基于深度学习的采煤机关键零件剩余寿命预测模型,实现实时监测数据驱动下的零件剩余寿命的在线预测;综合数字孪生体状态和剩余寿命值,实现采煤机健康状态预测。通过试验验证了该方法的有效性,为采煤机健康状态预测与管理提供新思路。

引用

页码：815 / 823

页数：9

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