Hybrid Cloud Architecture for Edge Analytics in Discrete Manufacturing
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Solution Overview
Problem
Industrial automation systems generate vast amounts of data, but access is typically limited to applications on the same network as the industrial controllers, restricting the use of this data for broader analytics and insights across geographically diverse facilities.
Innovation Solution
A hybrid data collection and analysis infrastructure that combines edge-level and cloud-level computing, where edge devices collect data, perform local analytics, and communicate with a cloud platform for higher-level analytics, enabling bi-directional communication for control instructions and data exchange.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If industrial data is collected and made accessible only on the same network as industrial controllers, then data security and network stability are maintained, but data utilization and analytics capability are restricted
Solution Approach 1:
The patent introduces an edge device as an intermediary component between industrial controllers and cloud analytics systems. The edge device collects data from controllers via protocol adapters, processes it locally, and transmits selected data to the cloud, thereby enabling broader data utilization without requiring all systems to be on the same network.
Solution Approach 2:
The system is segmented into multiple independent components: industrial controllers, protocol adapters, edge devices, and cloud analytics systems. Each segment operates independently with defined interfaces, allowing data to be accessed and analyzed across different networks while maintaining security and stability in each segment.
2Adaptability or versatility
If all industrial data is transmitted to cloud analytics systems, then comprehensive analytics capability is improved, but data transmission time and network bandwidth consumption increase
Solution Approach 1:
The edge device extracts and processes only the most relevant and valuable data for cloud transmission. Local analytics on the edge device filter out redundant information, sending only essential data to the cloud, thereby reducing transmission time and bandwidth consumption while maintaining comprehensive analytics capability.
Solution Approach 2:
The edge device performs preliminary data processing, filtering, and local analytics before transmitting data to the cloud. This preliminary action reduces the volume of data requiring transmission and prepares data in an optimized format, decreasing transmission time and improving overall system efficiency.
3Speed
If edge-level analytics are performed on all industrial data, then local response time is improved, but processing power requirements and computational load increase
Solution Approach 1:
The edge device performs analytics with quality tailored to local needs, processing data at appropriate levels of detail for immediate responses. Complex, resource-intensive analytics are performed selectively based on data importance and local requirements, optimizing the balance between response time and processing power consumption.
Data Source
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AI summary
A hybrid data collection and analysis infrastructure combines edge-level and cloud-level computing to perform high-level monitoring and control of industrial systems and processes. Edge devices located on-premise at one or more plant facilities can collect data from multiple industrial devices on the plant floor and perform local edge-level analytics on the collected data. In addition, the edge devices maintain a communication channel to a cloud platform executing cloud-level data collection and analytic services. As necessary, the edge devices can pass selected sets of data to the cloud platform, where the cloud-level analytic services perform higher level analytics on the industrial data. The hybrid architecture operates in a bidirectional manner, allowing the cloud-level and edge-level analytics to send control instructions to industrial devices based on results of the edge-level and cloud-level analytics.