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Reducing Downtime with Predictive Analytics
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
- 功能应用 - 远程监控系统
- 平台即服务 (PaaS) - 数据管理平台
- 传感器 - 温度传感器
适用行业
- 药品
适用功能
- 维护
用例
- 预测性维护
挑战
为了提高生产能力并避免停机,一家全球生物技术制造公司实施了 Seebo Predictive Analytics。
该公司的季度运营评估显示,生产期间的停机时间增加了 3.6%。这种停机时间源于生产线中一种产品的无法解释的粘度。
由此产生的反应器和生产线离心机之间的管道堵塞导致批量生产过程中更频繁的设备清洁程序和停机、高水平的浪费、产能下降和上市时间延长。
调查小组无法确定堵塞的原因,因为所有相关的生产参数都在批准的工作范围内。
客户
未公开
关于客户
该公司是一家总部位于美国的领先生物技术制造商,使用尖端的专有技术开发和制造营养成分。公司自成立以来发展迅速,产生了令人印象深刻的
解决方案
该公司决定投资工业 4.0 和预测分析,并寻找具有以下功能的解决方案:
- 将他们的制造专业知识与数据分析和机器学习相结合
- 为运营团队提供简单而准确的见解
- 预测未来的停机问题
Seebo 分析了生产线的历史数据和在线数据,并确定了导致堵塞的变量之间的相关性——混合持续时间、蒸馏时间和反应温度的具体变化。
基于这些发现,Seebo 解决方案可以在再次发生堵塞之前向运营团队提供预测警报。
借助 Seebo 解决方案,工厂恢复了预期的生产能力,生产团队能够确定正确的预测性维护计划。
收集的数据
Duration, Distillation time, Reaction temperature
数量效益
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