Enhancing moisture detection in coal gravels: A deep learning-based adaptive microwave spectra fusion method

Abstract

The accurate and effective detection of moisture in coal gravels is crucial. Conventional air oven-drying method suffers from prolonged processing times and their disruptive nature. This paper proposes a deep learning-based adaptive fusion method for multiple microwave spectra to non-destructively detect the moisture content of coal gravels. First, a purpose-built free-space measurement platform is employed to acquire microwave spectra of coal samples, encompassing the magnitude and phase spectra of reflection coefficients (S₁₁) and transmission coefficients (S₂₁). Subsequently, a Monte-Carlo cross-validation-based method is adopted to detect and eliminate outliers in the spectra. Furthermore, a novel feature extraction module is proposed, enhancing the traditional U-shaped network using residual learning (ResNet) and the convolutional block attention module (CBAM) to extract and reconstruct subtle spectral features. Inspired by the high-level data fusion, an adaptive spectra fusion method is then introduced that can autonomously balance the contributions between different spectra. The experimental results underscore the advantages of the proposed method, with narrow frequency intervals between 2.50-3.25 GHz, 3.75-4.00 GHz, and 4.75-5.00 GHz exhibiting superior detection accuracy compared to the entire frequency band, achieving R² = 0.9034, MAE = 1.0254, RMSE = 1.2948 and RPIQ = 6.0630.

Keywords: Coal moisture detection; Deep learning; Microwave spectroscopy; Monte-Carlo cross-validation; Spectra fusion.