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Predict to prevent: Transforming mining with machine learning
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
- 基础设施即服务 (IaaS) - 其他
适用行业
- 矿业
适用功能
- 维护
用例
- 预测性维护
挑战
矿业公司有大量数据可供使用。传感器在地下作业中似乎无处不在。但到目前为止，由于难以理解所有数据，矿业公司很难利用他们的所有数据。
那么对于矿业公司来说，最重要的数据是什么？简短的回答：资产。采矿业是资产最密集的行业之一。在开采链的每个环节——钻孔、切割、破碎、筛选和去除含矿岩石——重型设备都至关重要。它需要挨打。当设备发生故障，需要进行计划外的维护时，生产会受到影响，成本会上升，并且采矿资本效率的一个关键指标——整体设备效率 (OEE)——会下降。
客户
山特维克采矿和岩石技术
关于客户
Sandvik Mining and Rock Technology 是世界领先的采矿设备制造商。为采矿和岩石开挖提供设备和工具、服务和技术解决方案，包括凿岩、岩石切割、破碎和筛分
解决方案
IBM 使用机器学习算法在组件级别分析设备传感器数据。这个想法既基本又强大：如果您分析足够大的关于特定组件的维护和故障模式的数据集，您将能够准确预测该组件（例如，引擎的一部分）何时，变速器或刹车——很可能会出故障。这些模型产生的核心洞察力——每个组件的寿命预测——非常强大，因为它为操作员提供了他们需要的关键要素，以优化他们所有设备的整个操作中的定期维护实践。
收集的数据
Equipment Status, Overall Equipment Effectiveness, Disposal
运营影响
数量效益
相关案例.
Case Study
Underground Mining Safety
The goal was to produce a safety system to monitor and support underground mining operations; existing systems were either too simple (i.e. phone line) or overly complex and expensive, inhibiting deployment, and providing little-to-no support in event of an accident. Given the dangerous nature of the mining work environment and the strict regulations placed on the industry, the solution would have to comply with Mine Safety and Health Administration (MSHA) regulations. Yet the product needed to allow for simple deployment to truly be a groundbreaking solution - increasing miner safety and changing daily operations for the better.
Case Study
Mining Firm Quadruples Production, with Internet of Everything
Dundee Precious Metal’s flagship mine, in Chelopech, Bulgaria, produces a gold, copper, and silver concentrate set a goal to increase production by 30%. Dundee wanted to increase production quality and output without increasing headcount and resources, improve miner safety, and minimize cost.
Case Study
Fastenal Builds the Future of Manufacturing with MachineMetrics
Fastenal's objective was to better understand their machine downtime, utilization, quality issues, and to embrace cutting-edge manufacturing technology/process improvement capabilities to bring their team to the next level. However, there was a lack of real-time data, visualization, and actionable insights made this transition impossible.
Case Study
Joy Mining Systems
Joy equipment faces many challenges. The first is machine integration and control. The business end of the machine has a rapidly-spinning cylinder with 6-inch diamond-studded cutting teeth. It chews through rock at rates measured in tens of tons per minute. The system grinds through the rock in front, creating a rectangular mine tunnel. Hydraulic lifters support the ceiling as the machine moves forward. Automated drills and screws drive 3-ft long screws into the ceiling to stabilize it. The rock and coal fall into a set of gathering "fingers" below the cutting cylinder. These fingers scoop up the rock and coal and deposit it onto a conveyor belt. The conveyor passes under the machine and out the back. A train of conveyor belt cars, up to a mile long, follows the cutter into the mine. The rock shoots along this train at over 400 feet per minute until it empties into rail cars at the end. Current systems place an operator cage next to the cutter. Choking dust (potentially explosive), the risk of collapse and the proximity of metal and rock mayhem make the operator cage a hazardous location.
Case Study
Improved Monitoring in Industrial Manufacturing Facility
When your crane is moving tons of magma-hot iron, you can’t afford an unexpected failure. McWane Ductile knew monitoring the crane motor metrics within their facility could help prevent a mechanical failure that would strand an enormous bucket of molten metal overhead. Unfortunately, their legacy wired monitoring system couldn’t work with moving objects in this extreme environment. If they could integrate wireless capabilities into their existing equipment they could extend their monitoring capabilities without starting over from scratch.
