If there’s one thing industry’s embrace of cloud and edge computing technologies has shown us, it’s that hybrid systems—using both cloud and edge computing to handle data storage and analytics—are the preferred approach. As revealed in a2019 Automation World study，数字转换计划倾向于遵循典型的推出模式：第一阶段通常围绕云围绕云，以托管核心企业分析应用程序，以评估植物绩效或资产优化以减少生产停机时间。从那里开始，制造商倾向于投资边缘计算技术，以实时实时提供详细的现场分析。云还可以作为提供额外存储和计算可伸缩性的手段。
在开发中可以看到这种云和边缘计算的混合现实制造连接平台byGoogle Cloud和Litmus(a supplier of industrial edge computing technologies). The idea behind the Manufacturing Connect platform is to simplify the process of collecting data from disparate factory devices and exposing it to Google Cloud data and artificial intelligence (AI) applications.
增值税sal Shah, CEO of Litmus.
Features of the Manufacturing Connect platform include data collection and engineering, data visualizations and KPI (key performance indicator) dashboards, containerized application deployment, and machine learning model runtime. It also includes out-of-the-box integration with several analytics packages, such as Looker for BI and analytics, and Vertex AI for machine learning and AI.”
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This video covers the initial work to align石蕊边缘和Google Cloud Technologies. |
Explaining how Litmus Edge prepares data for Google Cloud analytics, Vatsal Shah, Litmus CEO, says, “Using Litmus technology, we collect data from industrial systems, normalize that data, and add all of the OT (operations technology) data variables for context locally. We push that data into the Google Cloud environment using publish/subscribe while Google can add more contextualized data on top at the enterprise level, such as from MES or ERP systems. The structured final data point is ready to use for analytics. All of this happens automatically but it is configurable if the user would like to change anything.”
Charlie Sheridan, technical director of industry solutions for manufacturing at Google Cloud.
Sheridan指出，制造连接可以在离散和工艺制造垂直领域中使用，例如汽车，航空，电子，半导体，医疗设备，药品，化学药品，塑料，食品和饮料以及包装和加工和处理。
Describing how end users can apply Manufacturing Connect, Sheridan says, “Once data is centralized and harmonized by the Manufacturing Data Engine and Manufacturing Connect, it can then be used to create custom dashboards to visualize key data—from factory KPIs (key performance indicators) such as overall equipment effectiveness (OEE), to individual machine sensor data, allowing them to uncover new insights and opportunities throughout the factory. These insights can then be shared across the enterprise and with partners.”
Sheridan指出的两种特定类型的应用程序是：
- Machine-level anomaly detection via Manufacturing Connects’ use of Google Cloud’s Time Series Insights, which analyzes real-time machine and sensor data such as noise, vibration, or temperature.
- 预测维护以降低停机时间和维护成本。Sheridan说，制造商可以使用ML（机器学习）型号和“可在几周内可部署”的高准确性AI优化。
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