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Mechanism characteristic analysis and soft measuring method review for ball mill load based on mechanical vibration and acoustic signals in the grinding process

机译:基于机械振动和磨削过程中的球磨机负荷的机制特征分析和软测量方法

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摘要

An operational optimization control for a mineral grinding process is limited by unmeasured load parameter inside a ball mill given its complex and unclear production mechanism. A mechanism characteristic analysis and soft measuring method for mill load parameter based on mechanical vibration and acoustic signals in the mineral grinding process is reviewed in this study. The modeling process based on the mechanical vibration and acoustic signals for the mill load parameters are summarized as a class of intelligent selective ensemble modeling problem. The applied soft measuring strategies for mill load parameter measurement are divided into three types, namely, off-line modeling, online modeling, and virtual sample generation, followed by a detailed discussion. Possible directions for mill load soft measurement techniques are provided for future research. These techniques include a vibration mechanism-based multi-component signal analysis, off-line intelligent ensemble soft measuring model based on simulation operational expert cognitive process, online updating strategy based on intelligently identified samples, and a mill load status intelligent recognition mode based on reinforcement learning strategy.
机译:鉴于其复杂和不明确的生产机制,矿物研磨过程的操作优化控制受球研磨机内的未测量负载参数受限。本研究综述了基于机械振动和矿物研磨过程中矿物振动和声学信号的轧机负荷参数的机理特性分析和软测量方法。基于机械振动和用于工厂负载参数的声学信号的建模过程总结为一类智能选择性集合建模问题。用于轧机负荷参数测量的应用软测量策略分为三种类型,即离线建模,在线建模和虚拟样本生成,然后进行详细讨论。为未来的研究提供了轧机负荷软测量技术的可能指示。这些技术包括基于振动机制的多分量信号分析,基于仿真运营专家认知过程的离线智能集合软测量模型,基于智能识别的样本的在线更新策略,以及基于加固的轧机负载状态智能识别模式学习策略。

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