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Mill Load Identification Method for Ball milling Process Based on Grinding Signal

机译：基于研磨信号的球磨过程轧机载荷识别方法

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摘要

Mill load is a key parameter for the safety and optimal control of the grinding process in mineral processing. Grinding sound signal is usually used to detect the mill load indirectly. However, the relationship between grinding sound signal and the mill load is really complicated. In this study, a mill load identification method is proposed by combining the machine learning algorithm with the sound recognition technology. Firstly, a geometric spectral subtraction denoising method based on auto regressive (AR) spectrum estimation is proposed to preprocess the grinding sound signal. Then, the ensemble empirical mode decomposition (EEMD) method is used to decompose the grinding signal. Suitable IMF components are then selected to reconstruct the grinding signal and the box fractal dimension feature is extracted. Finally, an optimized extreme learning machine (ELM) method was proposed to identify the mill load. The simulation results using industrial data show that the proposed method has better overall recognition accuracy compared with other machine learning methods.
机译：轧机负荷是矿物加工中磨削过程的安全和最优控制的关键参数。磨削声音信号通常用于间接检测轧机负荷。然而，研磨声音信号与轧机负载之间的关系非常复杂。在该研究中，通过将机器学习算法与声音识别技术相结合，提出了一种轧机负载识别方法。首先，提出了一种基于自动回归（AR）频谱估计的几何频谱减法去噪方法，以预处理研磨声音信号。然后，集合经验模式分解（EEMD）方法用于分解研磨信号。然后选择合适的IMF组分以重建研磨信号，并提取盒分形尺寸特征。最后，提出了一种优化的极限学习机（ELM）方法来识别轧机负荷。使用工业数据的仿真结果表明，与其他机器学习方法相比，该方法具有更好的整体识别准确性。

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