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Real-time Data Engineering for Industrial Systems Testing & Monitoring
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用行业
- 设备与机械
- 可再生能源
适用功能
- 维护
- 质量保证
用例
- 机器状态监测
- 实时定位系统 (RTLS)
服务
- 数据科学服务
- 培训
挑战
传感器以高频率从设备捕获物理数据，然后在基于桌面的系统上进行本地管理和分析。然后，他们的最终用户在他们的桌面上本地处理数据，并手动创建对设备状况及其故障模式的可见性。
他们决定自动化数据收集并启用基于云的数据处理以及基于 AI 的故障检测，同时使其更多地由数据驱动，而不是基于古老的规则。对此有一些挑战：
- 传统的预测工具难以扩展和部署
- 需要将预测分析嵌入到他们的应用程序中
- 使用现代数据平台进行数据准备、清理、选择正确的算法、对其进行训练和验证需求专业知识
- 平台和应用程序需要与所有硬件产品轻松集成
客户
未披露
关于客户
仪器工程公司为工业设备和可再生能源行业的领导者提供支持。他们提供智能传感器和硬件，帮助工业企业降低设备维护成本，提高机器产量，增加机器正常运行时间并确保工艺质量。
解决方案
采用最小可行产品方法来快速设计和构建一个数据平台，该平台拥有连接全球数百万传感器的能力，并使工业系统和设备能够更加智能地了解其故障、可用性和运行效率。
Saviant 与仪器工程公司合作，为他们未来十年的愿景开发所需的智能数据平台。与包括数据科学/机器学习顾问、技术架构师、物联网顾问在内的技术顾问团队合作，设计了一个平台，使
- 高性能数据工程并自动捕获数据、编排和分析
- 机器学习模型取代古老的“if-then 规则”故障检测方法
- 关于故障情况和警报的准确及时的警报和通知
运营影响
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