Hybrid Edge-Cloud Architecture for Multi-Facility Industrial Analytics
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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.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If industrial data is collected and stored locally on the same network as industrial controllers, then data access is reliable and fast, but data accessibility is restricted to applications on the same network
Solution Approach 1:
A cloud-based data storage service acts as an intermediary between industrial controllers and applications. The service receives data from controllers via network messages, stores it in cloud storage, and provides access to authorized applications through API calls, enabling remote access while maintaining data reliability
Solution Approach 2:
The system transitions data storage from a single local dimension (on-controller storage) to a distributed cloud dimension. Data is stored remotely in cloud storage services, allowing access from multiple locations and devices beyond the immediate network, effectively adding a new spatial dimension to data accessibility
2Productivity
If all industrial data is transmitted to cloud for analytics, then comprehensive analytics capability is improved, but network bandwidth consumption and data transmission time increase
Solution Approach 1:
The system extracts and transmits only relevant and necessary data to the cloud for analytics, rather than transmitting all industrial data. Data filtering and selection mechanisms identify which data points should be sent to cloud services, reducing transmission volume while maintaining analytics effectiveness
Solution Approach 2:
The system implements partial data transmission to the cloud, sending only the portion of data needed for specific analytics objectives. This partial action approach balances comprehensive analytics capability with reduced network bandwidth consumption and transmission time
3Adaptability or versatility
If a private network is deployed to enable cloud access from multiple facilities, then data accessibility across facilities is improved, but system complexity and deployment cost increase
Solution Approach 1:
Cloud-based data storage and API services act as intermediaries that enable multi-facility access without requiring complex private network infrastructure. Applications from different facilities can access industrial data through standardized cloud APIs, eliminating the need for dedicated private network connections between facilities
Solution Approach 2:
The cloud-based solution provides a universal access mechanism that serves multiple facilities through a single infrastructure. The cloud storage service and API gateway function as multi-functional platforms that handle data storage, retrieval, and authorization for numerous applications across different locations, replacing the need for separate private network implementations at each facility
Data Source
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 bi-directional 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.