Monitoring mill load is vital for the optimization and control of grinding process. This study proposed the use of an instrumented grinding media to assess solid loading inside a ball mill, with size and density of the instrumented ball comparable to that of the ordinary grinding media. The acceleration signal was captured by an embedded triaxial accelerometer. The signal was first detrended by a complete ensemble empirical mode decomposition and then reconstructed using a correlation coefficient method. The filling ratio, particle to ball ratio, time domain features and sample entropy are features extracted from the signal, providing input to a support vector machine (SVM) learning model. Grinding experiments with different loads were conducted. The typical loading level was classified according to grinding efficiency index and associated power consumption. Different methods were adopted to determine the optimal values of parameters in the SVM model, including particle swarm optimizer (PSO), genetic algorithm (GA), and grid search (GS). The results showed that the accuracy of particle swarm optimizer can reach 96.67%. This study demonstrates the potential of using instrumented grinding media for real-time characterization of mill feed and operation monitoring.

监测磨机负载对于优化和控制磨矿过程至关重要。本研究建议使用仪表化研磨介质来评估球磨机内的固体负载，仪表化球的尺寸和密度与普通研磨介质的尺寸和密度相当。加速度信号由嵌入式三轴加速度计捕获。信号首先通过完整的集合经验模式分解去趋势，然后使用相关系数方法重建。填充率、粒子球比、时域特征和样本熵是从信号中提取的特征，为支持向量机 (SVM) 学习模型提供输入。进行了不同载荷的磨削实验。典型负载水平根据研磨效率指数和相关功率消耗进行分类。采用不同的方法来确定SVM模型中参数的最优值，包括粒子群优化器（PSO）、遗传算法（GA）和网格搜索（GS）。结果表明，粒子群优化器的准确率可以达到96.67%。这项研究证明了使用仪表化研磨介质实时表征磨机进料和运行监控的潜力。