Integrated computation and communication is some of the elements of industrial automation systems designed to perform fully digitalized operations. The ISA-95 industrial automation standard establishes manufacturing execution systems (MES) and enterprise moving systems (ERP) as the ones that can be directly accessed from the cloud due to low-time requirements (Dai et al.). Systems below the Industrial Gateway have high real-time requirements, but these constraints could be reduced as computing power, storage space, and central processing unit (CPU) have become more cost-effective. Industrial edge computing, which requires more time, can optimize data, computation, and storage resources through smart gateways and local clouds (Dai et al.). According to Dai et al., it faces some challenges, including lack of compatibility between existing and emerging industrial fieldbuses, difficulties with data mining on edge notes, real-time constraints of fault detection and data acquisition, as well as providing different access levels without affecting real-life performance.
Architecture for a typical hybrid industrial cloud and edge computing systems includes three levels. The top layer covers design, management (maintenance), and manufacturing (Dai et al.). The middle layer manages data acquisition, balances resources, and develops algorithms (Dai et al.). The base layer performs different functionalities and contains multiple edge computing nodes (ECN) (Dai et al.). TIndustrialedge computing can manage local resources as well as other devices and connections through data processing and analysis (Dai et al.). It can reduce the cost of data mining and quality tracing as massive data move between cloud and edge. As a supplement, industrial edge computing can ensure interoperability, self-optimization, and reduction of industrial clouds’ workload through real-time data processing.
References
Dai, Wenbin, et al. “Industrial Edge Computing: Enabling Embedded Intelligence.” IEEE Industrial Electronics Magazine, vol. 13, no. 4, 2019, pp. 48-56. doi: 10.1109/MIE.2019.2943283.