Optimizing mill performance through machine learning empowered sensing data analytics

半自磨机（SAG）在采矿作业中用于将矿石破碎成均匀的骨料粒度以供进一步加工。物理研磨过程在一个大桶中进行，在大桶中加入矿石和钢球。为防止损坏滚筒，应插入金属或衬垫。更换衬板的成本取决于磨机的停机时间和更换部件，因此尽可能晚更换衬板是经济的，而且还能最大限度地提高磨机的产量。因此，本研究的目的是最大限度地提高衬板的使用寿命和磨机的产量。为了实现这一目标，在作业过程中通过在线监测来预测尾管磨损是至关重要的。这项研究将对多个监测传感器获得的数据进行综合分析，如振动、应变和螺栓张力测量。特别是，我们计划使用由合作伙伴组织Promet专利的电磁声换能器（EMAT）。实时测量衬板厚度。EMAT传感器具有野外作业的优点。EMAT测量数据将与其他传感器数据融合，以预测衬板状态和剩余使用寿命，并用于设计磨机系统的最优控制策略。本研究将探索数据驱动建模和基于深度学习的方法来实现采矿业磨机的预测性维护。

Semi-autogenous (SAG) mills are employed in mining operations to break ore into a uniform aggregate size for further processing. The physical grinding process is carried out in a large drum where the ore and steel balls are added. To prevent damage to the drum, metal or liners are inserted. The costs of liner replacement depend on mill downtime and replacement parts so it is economical to change the liner as late as possible, but also maximize the mill output. Thus, the objective of this research is to maximize both the liner service life and the mill production. To achieve this goal, it is critical to predict the liner wear through the online monitoring during operation. This research will perform a comprehensive analysis of the data acquired by multiple monitoring sensors, such as vibration, strain, and bolt tension measurements. Particularly, we plan to use an electromagnetic acoustic transducer (EMAT) patented by the partner organization, Promet.ai, to measure the liner thickness in real-time. The EMAT sensor has the advantages to operate in field. The EMAT measurement together with other sensor data will be fused to predict the liner condition and remaining useful life, which is utilized to design a strategy for optimal control of the mill system. Data-driven modelling and deep learning based approaches will be explored in this study to achieve the predictive maintenance of the grinding mill in mining industry.