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研究由动态模型驱动的光纤传感滚筒故障诊断方法

Hongyu Zhang^1,2,3,4,5, Zhiyong Yang^{6,7,8,9,10,11}, Fuli Wu^6,7,8,9,10

¹Xinjiang Tianchi Energy Co., Ltd., Changji, 831100, China. hongyu158158@163.com.

Scientific reports

|October 23, 2025

概括

这项研究引入了一种光纤传感方法,用于诊断露天矿山的输送滚筒故障. 新的动态模型驱动方法实现了96.7%的准确性,提高了矿山安全,并使无人操作成为可能.

科学领域:

工程工程师工程师工程师
材料科学材料科学材料科学
数据科学数据科学数据科学

背景情况:

开采矿需要对输送机滚筒断层进行强有力的监测,特别是在极端天气条件下.
现有的方法可能不足以实时,在动态的采矿环境中进行非侵入性故障检测.

研究的目的:

开发和验证一种新的光纤传感方法,用于诊断露天采矿中的输送滚筒故障.
提高关键采矿基础设施故障诊断的准确性和可靠性.

主要方法:

分析光纤散射光和机械振动之间的合机制.
使用动态模型构建一个非侵入性的实时监控系统.
应用短时间里叶变换 (STFT) 和Mel算法用于信号处理.
开发一个改进的GoogleLeNet LSTM混合深度学习模型用于故障分类.

主要成果:

拟议的光纤传感方法成功识别了输送机滚筒故障.
改进的GoogLeNet LSTM模型在故障诊断方面实现了96.7%的测试准确率.
这比基本的卷积神经网络 (CNN) 模型的准确度提高了83.7%.

结论:

开发的动态模型驱动光纤传感方法为输送滚筒故障诊断提供了可靠的解决方案.
这项技术为推进开采矿运输系统中无人操作提供了关键的技术支持.
取得的高精度证明了采矿操作中提高安全性和效率的潜力.

关键词:

在 DY-GoogLeNet-LSTM 搜索中使用动力学动力学动力学光纤传感传感器是指光纤传感器.谷歌的网络-LSTM

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