基于多重线性回归模型的TBM主光束的振动预测和分析
Yalei Yang1, Lijie Du2, Qingwei Li1
1School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China.
Scientific reports
|February 12, 2024
概括
监测道钻井机 (TBM) 的振动是一项挑战. 这项研究开发了一种新的多重回归模型,使用关键道和岩石参数来预测TBM振动,实现实时设备状态评估的错误率低于12%.
科学领域:
- 地质技术工程 地质技术工程
- 机械工程 机械工程
- 预测性维护是指预测性维护.
背景情况:
- 道钻机 (TBM) 的振动监测在现场操作中很困难.
- 有限的研究存在于预测TBM振动.
- 实时评估TBM设备状态至关重要.
研究的目的:
- 调查道开采参数,周围岩石条件和TBM振动之间的关系.
- 开发一个TBM振动的预测模型.
- 为了在没有传统监控系统的情况下实现实时的TBM状态确定.
主要方法:
- 在实地道测试期间从TBM收集的振动数据.
- 分析了道参数 (透率,切割头转速,扭矩,推力) 和振动参数之间的关系.
- 开发了基于场透和切头驱动功率指数的多重回归预测模型.
主要成果:
- 穿透,切割头速度,扭矩和推力显著影响TBM振动.
- 场透指数和切割头驱动力指数与加速的根平均平方强烈相关.
- 开发的多重回归模型实现了不到12%的预测误差.
结论:
- 一个新的多重回归模型可以使用特征参数准确预测TBM振动.
- 这种方法允许实时预测TBM振动,增强设备状态监控.
- 这种方法提供了一个有价值的替代方案,用于传统的监控系统TBMs.
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