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Updated: Jun 3, 2025

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设计一个多变体的带式输送机置站检测和识别系统,并进行可扩展性分析
Kyeong Su Shin1, Younho Nam2, Young-Joo Suh1
1Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang 37673, Republic of Korea.
检测出故障的带式输送机动器对于维护至关重要. 一个使用传感器数据的新深度学习系统准确地识别了置摊位,但可扩展性需要管理大型输送系统的网络带宽和能源.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 传感器技术 传感器技术
背景情况:
- 皮带输送机机是关键部件,当它们失效时,可能会造成严重的皮带损坏.
- 早期和精确检测机故障对于高效维护带式输送系统至关重要.
- 现有的监测方法可能缺乏广泛的输送机装置所需的可扩展性和准确性.
研究的目的:
- 实施和评估一种多变量深度学习模型,用于检测停滞不前的带式输送机机.
- 评估系统的性能,重点关注准确性,网络带宽和能源消耗.
- 确定大规模部署用于监测几公里长的输送系统的可行性.
主要方法:
- 利用加速度计和麦克风传感器数据来训练一个多变量深度学习模型.
- 专注于为广泛的输送机网络开发可扩展的系统架构.
- 分析了系统准确性,网络带宽使用情况和能源需求.
主要成果:
- 拟议的深度学习系统能够准确地检测机.
- 可扩展性分析表明大型输送机监控的可行性.
- 网络带宽和能源消耗是实际实施的关键因素.
结论:
- 通过开发的深度学习系统,可以实现准确的,大规模的置机停机检测.
- 有关网络带宽和能源预算的仔细规划是成功部署的必要条件.
- 该系统为广泛的带式输送机运营中主动维护提供了一个有前途的解决方案.
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