预测预防：用机器学习改变采矿
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
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
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。
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
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