Safety Measurements and Risk Assessment of Coal Mining Industry Using Artificial Intelligence and Machine Learning

Sundas Matloob, Yang Li, Khurram Zaman Khan
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引用次数: 10

Abstract

Safety is a legal requirement of mining industry. Effective safety measurements and risk management can serve the mine administration to lessen the dangers recognized in the mine. Autonomous technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) models have been integrated with the mining industry to ensure a safe working environment since a decade ago in the form of fully unmanned workfaces and mining robots. These autonomous technologies provide effective risk assessment as well as many economic profits such as cost reduction, continuous production, reducing labors exposure in dangerous environments, and enhanced protection. The main objective of this paper is to highlight the unidentified hazards during mining and find the solutions to those hazards by using AI and Machine Learning. In addition, this paper will also contribute a worthy resource for explaining the AI advancements in risk assessment in the mining industry. Another advantage of this paper is to fill the gaps in modern techniques and AI advancements in the safety measurements of coal mining industry.
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基于人工智能和机器学习的煤矿安全测量与风险评估
安全是采矿业的法律要求。有效的安全措施和风险管理可以为矿务部门服务,减少矿井中已知的危险。自十年前以来,人工智能(AI)和机器学习(ML)模型等自主技术已与采矿业相结合,以完全无人驾驶的工作面和采矿机器人的形式确保安全的工作环境。这些自主技术提供了有效的风险评估以及许多经济效益,例如降低成本,连续生产,减少工人在危险环境中的暴露,以及增强保护。本文的主要目的是突出挖掘过程中未识别的危险,并通过人工智能和机器学习找到这些危险的解决方案。此外,本文还将为解释人工智能在采矿业风险评估中的进展提供有价值的资源。本文的另一个优点是填补了现代技术和人工智能在煤矿安全测量方面的空白。
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