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Experimental analysis of wet mill load based on vibration signals of laboratory-scale ball mill shell

机译:基于实验室规模球磨机壳体振动信号的湿磨机负荷实验分析

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摘要

Real-time measurement of the mill load is the key to improve the production capacity and energy efficiency for the grinding process. In this paper, experimental analysis of the wet mill load based on the vibration signals of the laboratory-scale ball mill shell is presented. A series of experiments are conducted to investigate the vibration characteristics corresponding to different grinding conditions such as dry grinding, wet grinding and water grinding. The power spectral density of the vibration signals is systematically interpreted. Experimental results show that the rheological properties of the pulp affect the amplitude and frequency of the vibration signal. The most important conclusion is that the frequency range of the shell vibration of the laboratory wet mill can be divided into three parts, namely natural frequency band, main impact frequency band and secondary impact frequency band. Finally, soft-sensor models between vibration signal and mill operating parameters of mill load are established using genetic algorithm-partial least square (GA-PLS) technology. After more work on industry scale ball mill is done, the soft-sensor modeling based on the mill shell vibration for operating parameters of mill load will improve the performance of the ball mill in the grinding process.
机译:磨机负荷的实时测量是提高研磨工艺的生产能力和能源效率的关键。本文基于实验室规模的球磨机壳体的振动信号,对湿磨机负荷进行了实验分析。进行了一系列实验以研究与不同磨削条件(例如干磨,湿磨和水磨)相对应的振动特性。系统地解释了振动信号的功率谱密度。实验结果表明,纸浆的流变特性会影响振动信号的振幅和频率。最重要的结论是,实验室湿磨机的机壳振动频率范围可以分为三个部分,即自然频带,主冲击频带和次冲击频带。最后,利用遗传算法-偏最小二乘(GA-PLS)技术建立了振动信号与轧机负荷运行参数之间的软传感器模型。在完成工业规模球磨机的更多工作之后,基于磨机壳振动的磨机负荷运行参数的软传感器建模将改善球磨机在研磨过程中的性能。

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