Intelligent fill level monitoring device for ball mill vibration signals based on fiber optic microphones

YANG Hua; SHI Xiaoju; WANG Daoping; ZHU Binzhi; XING Jichuan

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Intelligent fill level monitoring device for ball mill vibration signals based on fiber optic microphones

Beijing Institate of Technology 85th Anniversary | 更新时间：2025-09-22

- Intelligent fill level monitoring device for ball mill vibration signals based on fiber optic microphones
- OPTICAL TECHNIQUE Vol. 51, Issue 5, Pages: 549-556(2025)
- 作者机构：
  
  1.国能四川华蓥山发电有限公司，四川达州 635214
  2.北京理工大学光电学院，北京 100081
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP23;TP391.4
- Received：11 October 2024，
  
  Revised：2025-03-11，
  
  Published：20 September 2025
- 稿件说明：
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杨华,施小驹,王道平等.基于光纤麦克风的球磨机声纹信号智能料位监测装置[J].光学技术,2025,51(05):549-556.

YANG Hua,SHI Xiaoju,WANG Daoping,et al.Intelligent fill level monitoring device for ball mill vibration signals based on fiber optic microphones[J].OPTICAL TECHNIQUE,2025,51(05):549-556.
杨华,施小驹,王道平等.基于光纤麦克风的球磨机声纹信号智能料位监测装置[J].光学技术,2025,51(05):549-556. DOI：

YANG Hua,SHI Xiaoju,WANG Daoping,et al.Intelligent fill level monitoring device for ball mill vibration signals based on fiber optic microphones[J].OPTICAL TECHNIQUE,2025,51(05):549-556. DOI：

摘要

文章提出了一种基于光纤麦克风和深度学习的球磨机声纹信号智能监测装置。装置采用Φ-OTDR光纤传感技术获取高质量声纹信号，通过数据预处理和时频分析提取信号特征，构建融合卷积神经网络（CNN）和长短时记忆网络（LSTM）的深度学习模型实现球磨机料位精确监测。系统包括声学传感、数据预处理、特征提取与分类识别三个功能模块，可实现从信号采集到故障诊断的全流程自动化。装置克服了传统球磨机料位监测方法在恶劣工况下的局限性，为火电厂球磨机的智能运维提供了新的解决方案。研究成果可推广应用于其他旋转机械的状态监测，具有重要的理论意义和应用价值。

Abstract

An intelligent monitoring device for ball mill vibration signals based on fiber optic microphones and deep learning is proposed. The device utilizes Φ-OTDR fiber optic sensing technology to acquire high-quality vibration signals

extracts signal features through data preprocessing and time-frequency analysis

and constructs a deep learning model integrating Convolutional Neural Network （CNN） and Long Short-Term Memory （LSTM） network to achieve precise monitoring of ball mill material level. The system comprises three functional modules: acoustic sensing

data preprocessing

and feature extraction and classification recognition

enabling full-process automation from signal acquisition to fault diagnosis. This device overcomes the limitations of traditional monitoring methods under harsh working conditions

providing a new solution for intelligent operation and maintenance of ball mills in thermal power plants. The research results can be extended to condition monitoring of other rotating machinery

with significant theoretical and practical value.

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references

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