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Smart automated noise policy monitoring and feedback control system for mining application

机译:用于采矿应用的智能自动噪声策略监控和反馈控制系统

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This work develops an integrated smart noise monitoring model that can be used to monitor mine workers on site. The system provides mine administrators with the current state of hearing of the individual employee. The information obtained from the model can be used by the administration, for various purposes including monitoring and provision of early intervention. The main purpose of the model is to protect mining employees from experiencing significant hearing threshold shifts which can result in permanent hearing loss. The novelty of this model is the formulation of noise policy management model using feedback control with policies acting as actuators. Mining noise policies, Code of Practice and milestones are used in the selection of the appropriate controllers to be used in the system. Feedback is used in the system to compare the threshold output to the baseline. The output of the system is further processed using Internet of Things to ensure effective communication between the mine employees and the administrators. The model was validated using open source data from a real deep gold mine in South Africa. The results were generated using Matlab as a platform and were found to be comparable to the existing static model and more accurate. Future improvements to this work is to include artificial intelligence and machine learning concepts to the system to make it more robust.
机译:这项工作开发了一个集成的智能噪声监测模型,可用于监控现场的矿工。该系统提供了矿区管理员,并具有当前员工的听证状态。从该模型获得的信息可以由管理,以便各种目的,包括监测和提供早期干预。该模型的主要目的是保护采矿员工经历显着的听力阈值班,这可能导致永久性听力损失。该模型的新颖性是使用反馈控制的噪声策略管理模型的制定,并使用作为执行器的政策。采矿噪声策略,实践代码和里程碑用于选择系统中使用的适当控制器。在系统中使用反馈以将阈值输出与基线进行比较。使用物联网进一步处理系统的输出,以确保矿山员工和管理员之间的有效沟通。使用来自南非真正的深金矿的开源数据验证了该模型。结果是使用MATLAB作为平台产生的,发现与现有的静态模型相当,更准确。未来对这项工作的改进是将人工智能和机器学习概念与系统一起,使其更加强大。

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