天然气和石油开采设备的预测性维护软件
技术
- 分析与建模 - 预测分析
- 功能应用 - 企业资产管理系统 (EAM)
- 基础设施即服务 (IaaS) - 其他
适用行业
- 石油和天然气
适用功能
- 维护
用例
- 预测性维护
客户
贝克休斯
关于客户
在需求高峰期，贝克休斯的工作人员昼夜不停地开采石油和天然气储层。在一个井场，多达 20 辆卡车可以同时运行，容积式泵在高处注入水和沙子的混合物
挑战
如果活动现场的卡车出现泵故障，贝克休斯必须立即更换卡车以确保连续运行。向每个站点发送备用卡车会使公司损失数千万美元的收入，如果这些卡车在另一个站点积极使用，这些卡车可能会产生这些收入。无法准确预测阀门和泵何时需要维护是其他成本的基础。过于频繁的维护会浪费精力，并导致零件在仍然可用时被更换，而过于不频繁的维护可能会损坏泵而无法修复。
解决方案
使用 MATLAB 分析近 1 TB 的数据，并创建一个可以在机器故障发生之前预测它们的神经网络
运营影响
数量效益
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