Abstract Online monitoring load parameters inside the ball mill is the key to improving the production quality and quantity of the mineral grinding process. In this paper, the experimental analysis of wet mill load parameter (MLPs) based on multiple channel mechanical signals is presented. A series of experiments is conducted to investigate the mechanical frequency spectrum characteristics in terms of different grinding conditions, such as only-ball, -mineral, or –water load change. Based on power spectra density (PowSD), multiple channel mechanical signals are interpreted for different MLPs, i.e., mineral-to-ball volume ratio (MBVR), pulp density (PD), and charge volume ratio (CVR), in detail. Experimental results show that the PowSDs of these mechanical signals are positively correlated with CVR and negatively correlated with MBVR and PD. Further, the generation mechanism of these mechanical signals is qualitatively analyzed, and a new measurement method for the contribution rate of multiple channel mechanical signals, i.e., combination estimation index, is proposed. The results show the different contribution rates of these signals to various MLPs under varied grinding conditions. Appropriate mechanical channels for different MLPs must be selected to construct an effective MLP forecasting model.

中文翻译：

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基于多通道机械信号的湿磨机负荷参数多磨矿工况试验分析

摘要 在线监测球磨机内部负荷参数是提高矿物粉磨过程生产质量和数量的关键。在本文中，提出了基于多通道机械信号的湿磨负荷参数 (MLP) 的实验分析。进行了一系列实验以研究在不同研磨条件下的机械频谱特性，例如仅球、矿物或水负载变化。基于功率谱密度 (PowSD)，详细解释了不同 MLP 的多通道机械信号，即矿物球体积比 (MBVR)、矿浆密度 (PD) 和电荷体积比 (CVR)。实验结果表明，这些机械信号的 PowSDs 与 CVR 呈正相关，与 MBVR 和 PD 呈负相关。进一步对这些机械信号的产生机理进行定性分析，提出了一种新的多通道机械信号贡献率测量方法，即组合估计指标。结果显示了这些信号在不同研磨条件下对各种 MLP 的不同贡献率。必须为不同的 MLP 选择合适的机械通道，以构建有效的 MLP 预测模型。