【摘要】 针对矿用带式输送机托辊故障定位存在频率混叠、定位误差较大的问题，提出一种基于函数反卷积优化的矿用带式输送机托辊故障声源定位方法。该方法重构和解析了传统故障声源定位成像函数中的互谱矩阵函数和点扩散函数，通过优化声源定位函数，提高对故障信号的分辨能力，从而有效提升定位精度，确保能够准确识别托辊的故障位置。首先，计算初始定位声源图中由旁瓣引起的峰值互谱矩阵函数，将去除峰值互谱矩阵后的修正互谱矩阵函数作为重构优化的目标互谱矩阵函数；其次，对重构后的互谱矩阵函数进行特征值分解，计算其特征向量和特征值并构成酉矩阵和对角矩阵；最后，对声源定位函数进行元素级幂次变换，对互谱矩阵函数执行元素级根次变换，得到重构解析后的点扩散函数，形成了函数反卷积优化的托辊故障声源定位方程组；为提升函数反卷积优化的求解效率，采用加速贪婪迭代更新策略以快速收敛至声源定位解。针对故障声源频率混叠的问题，采用改进麻雀算法优化的变分模态分解和改进多尺度双阈值小波协同分层自适应去噪的方法，增强声源故障特征频率的提取。实验结果表明：相较于常见托辊故障声源定位方法，该方法能够有效去除声源成像图中噪声的旁瓣干扰和提高托辊故障声源定位精度；优化后的算法在故障定位精度上达到了94.42%,计算效率提高了约3.8倍；协同分层自适应去噪的信噪比与峰值信噪比分别提高了88.25%和56.06%,具有较好的精度增益和效率增益。更多还原

【Abstract】 A method for mine roller fault acoustic source localization based on function deconvolution optimization was proposed to address the problems of frequency aliasing and large localization error in mine belt conveyor systems. The cross-spectral matrix function and the point spread function in the traditional fault acoustic source imaging function were reconstructed and analyzed. By optimizing the acoustic source localization function, the resolution of fault signals was improved, thereby enhancing localization accuracy and ensuring accurate identification of mine roller fault positions. First, the peak cross-spectral matrix function caused by sidelobes in the initial localization acoustic source map was calculated. The corrected cross-spectral matrix function, obtained by removing the peak cross-spectral matrix, was used as the target cross-spectral matrix function for reconstruction and optimization. Then, the reconstructed cross-spectral matrix function was decomposed into eigenvalues, and its eigenvectors and eigenvalues were used to form a unitary matrix and a diagonal matrix. Finally, an element-wise power transformation was performed on the acoustic source localization function, and an element-wise root transformation was applied to the cross-spectral matrix function to obtain the reconstructed and analyzed point spread function. A function deconvolution-optimized equation set for mine roller fault acoustic source localization was then established. To improve the computational efficiency of the function deconvolution optimization, an accelerated greedy iterative update strategy was adopted to achieve fast convergence to the localization solution. To address the problem of frequency aliasing in fault acoustic sources, a variational mode decomposition optimized by an improved sparrow search algorithm and an improved multi-scale dual-threshold wavelet cooperative hierarchical adaptive denoising method were applied to enhance the extraction of fault feature frequencies. Experimental results show that, compared with common mine roller fault acoustic source localization methods, the proposed method effectively suppresses sidelobe interference in acoustic imaging maps and improves the localization accuracy of mine roller faults. The optimized algorithm achieves a localization accuracy of 94.42% and increases computational efficiency by approximately 3.8 times. The signal-to-noise ratio and peak signal-to-noise ratio of the cooperative hierarchical adaptive denoising are improved by 88.25% and 56.06%, respectively, indicating significant gains in both accuracy and efficiency.更多还原