基于一维卷积神经网络的扫描机输送机状态感知研究
Jie Lu1,2, Zhenlin Liu1, Chenhui Han1
1School of Coal Engineering, Datong University Shanxi Province, Datong, China.
PloS one
|October 18, 2024
概括
这项研究引入了一种使用一维卷积神经网络 (1DCNN) 评估刀输送机健康状况的新方法. 1DCNN模型准确地识别了设备的健康状况,改善了采矿操作.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 目前的刀输送机健康评估严重依赖于专家知识.
- 为采矿设备建立准确的降解模型具有挑战性.
- 客观的健康状况监测对于运营效率和安全至关重要.
研究的目的:
- 开发一种自动化方法来评估刀输送机的健康状况.
- 克服基于专家的主观评估的局限性.
- 为实时设备健康监测提供数据驱动的方法.
主要方法:
- 利用了四个预处理的监控信号,来自刀输送机.
- 开发并应用了一维卷积神经网络 (1DCNN) 模型.
- 提取有效特征并将其映射到设备健康状态.
主要成果:
- 提出的1DCNN方法在识别健康状况方面实现了98.9%的高准确率.
- 证明了有效的特征提取和健康状况识别能力.
- 对比分析证实了该方法的卓越性能.
结论:
- 基于1DCNN的方法提供了一个有效的解决方案,用于刀输送机健康评估.
- 为煤矿健康管理提供了宝贵的技术支持.
- 允许对采矿设备进行更可靠和客观的监控.
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