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通过噪音倾听:在煤矿钻井管道中强大的超声波裂纹检测,使用滑窗RMS和CNN
Xianghui Meng1, Hua Luo1, Fengli Lei1
1State Key Laboratory of Coal Mine Disaster Prevention and Control, CCTEG Chongqing Research Institute, Chongqing 400039, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究引入了一种新的超声波裂检测方法,用于煤矿钻井管道,使用滑动窗根平均平方 (SWRMS) 指数和卷积神经网络 (CNN). 该方法即使在杂的环境中也能准确识别裂,从而提高设备的安全性.
科学领域:
- 地质物理学和机械工程
- 非破坏性测试 不破坏性测试
- 机器学习应用 机器学习应用
背景情况:
- 煤矿钻井管道面临严重的运行压力,导致微裂和潜在的设备故障.
- 现有的裂检测方法与复杂的地下环境和噪音干扰作斗争.
研究的目的:
- 为煤矿钻井管道开发一个准确和强大的超声波裂检测框架.
- 通过智能诊断来提高采矿设备的安全性和可靠性.
主要方法:
- 采用滑动窗根平均平方 (SWRMS) 指数用于超声波信号特征提取,捕获裂位置和大小.
- 开发了一个卷积神经网络 (CNN) 模型,用于在高噪音条件下智能裂分类.
- 采用数据增强与交替的噪声水平来模拟钻井管线程的真实干扰.
主要成果:
- 该SWRMS指数有效地代表了裂的空间位置和大小.
- 美国有线电视新闻网 (CNN) 模型的分类准确度达到94.4%,即使在75%的噪音水平下也是如此.
- 与传统方法相比,表现出优越的识别性能和噪声稳定性.
结论:
- 建议的超声波裂纹检测方法在煤矿钻井管道的准确性和稳定性方面具有显著的优势.
- 该框架为采矿操作中的实时监控和智能诊断提供了有效的支持.
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