Intelligent Level Monitoring Device for Ball Mill Vibration Signals Based on Fiber Optic Microphones

This paper proposes an intelligent monitoring device for ball mill vibration signals based on fiber optic microphones and deep learning. 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 fill 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.