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相关概念视频

Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

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Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
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基于深森林的盾牌机芯的损害状态识别和量化方法

Huawei Wang1,2, Qiang Gao3, Sijin Liu1,2

  • 1China Railway 14th Bureau Group Co., Ltd., Jinan 250011, China.

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概括

本研究介绍了一种使用深森林 (DF) 模型的智能方法,以准确识别和量化道钻机中磁盘切割器损坏. 该方法提高了道工程的安全性和效率.

关键词:
森林深处的森林深处的森林.热点热点热点伤害伤害机器学习是机器学习.盾牌机床的烤箱是一个烤箱.信号处理 信号处理 信号处理磨损监控监控磨损的使用情况.

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科学领域:

  • 工程 工程师 工程师 工程师
  • 机器学习 机器学习
  • 材料科学 材料科学 材料科学

背景情况:

  • 磁盘切割器损坏严重影响道挖掘的安全性和效率.
  • 手动检查是有风险和低效的;目前的检测方法缺乏准确性和实时能力.
  • 现有的方法往往无法提供定量化磨损评估.

研究的目的:

  • 开发一种智能方法来识别和定量评估磁盘切割器损伤.
  • 通过可靠的切割器状况监测,提高道开采操作的安全性和效率.
  • 克服现有的手动和自动检查技术的局限性.

主要方法:

  • 建立了一个流传感器校准平台,以精确量化磨损.
  • 预处理的传感器数据使用过和脉冲边缘检测来提取损坏特征.
  • 采用深森林 (DF) 分类模型来识别损坏状态 (正常,边缘破碎,异常磨损,裂).
  • 使用DF回归模型对损坏大小的持续定量预测.

主要成果:

  • 该DF分类模型实现了高精度 (98%的培训,96%的验证/测试) 和F1分数 (>0.96).
  • 该DF回归模型表现出极好的性能,R2为0.9940和RMSE为0.4051.
  • 综合方法提供了定性识别和定量评估磁盘切割器损伤.

结论:

  • 拟议的基于深森林的方法提供了精确可靠的识别和定量评估磁盘切割器损伤.
  • 这种智能系统为在道项目中及时维护和更换切割机提供了强大的决策支持.
  • 该方法表现出强大的性能和通用性,解决了道工程中的关键需求.