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

机译：采矿应用的智能自动化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视模型，该模型可用于监视现场的矿山工人。该系统为矿山管理员提供单个员工的当前听觉状态。从模型中获得的信息可以由主管部门用于各种目的，包括监视和提供早期干预。该模型的主要目的是保护采矿员工免受严重的听力阈值变化的影响，这可能会导致永久性听力损失。该模型的新颖之处在于使用反馈控制和策略作为执行器来制定噪声策略管理模型。在选择要在系统中使用的适当控制器时，将使用采矿噪声策略，操作规范和里程碑。系统中使用反馈来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通信。该模型已使用来自南非一家真正的深金矿的开源数据进行了验证。结果是使用Matlab作为平台生成的，结果可与现有的静态模型进行比较，并且更加准确。这项工作的未来改进是将人工智能和机器学习概念纳入系统中，以使其更加强大。

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