Combined with wavelet time frequency analysis and lightweight deep convolutional networkintelligent recognition of coal rock acoustic emission signal

Key Points

• Effective recognition of coal rock acoustic emission signals improves detection accuracy.
• Wavelet transforms and deep convolutional networks achieved an accuracy rate of over 90%.
• Analysis utilized advanced signal processing and feature extraction techniques to refine data interpretation.
• This method provides a promising approach for real-time monitoring in mining operations.