Benefits of Edge Computing in Industrial Automation

Benefits of Edge Computing in Industrial Automation

Benefits of Edge Computing in Industrial Automation

  • Reduced Latency: Edge computing brings data processing closer to the source, minimizing the time required for data to travel back and forth to a central server, resulting in lower latency and faster response times.
  • Real-time Decision-making: By processing data locally, edge computing enables real-time analysis and decision-making, allowing industrial automation systems to respond quickly to changing conditions and optimize processes in real-time.
  • Enhanced Reliability: Edge devices can continue to operate autonomously even in the event of a network failure or disruption, ensuring uninterrupted operation and reducing downtime in industrial automation.
  • Bandwidth Optimization: Edge computing reduces the amount of data that needs to be transmitted to a central server or cloud, optimizing bandwidth usage and reducing network congestion.
  • Scalability: Edge computing allows for distributed processing and storage, making it easier to scale industrial automation systems as the number of connected devices and data volume increases.
  • Data Privacy and Security: Edge computing enables local data processing, reducing the need to transmit sensitive data over external networks, thus enhancing data privacy and security in industrial automation applications.
  • Cost Efficiency: With edge computing, organizations can reduce the costs associated with data transmission and storage, as only relevant data is transmitted to the cloud or central servers, and edge devices can perform local processing tasks.
  • Offline Operation: Edge devices can operate independently without relying on constant internet connectivity, ensuring uninterrupted operation even in environments with limited or intermittent network connectivity.
  • Compliance with Regulatory Requirements: Edge computing allows organizations to comply with data sovereignty regulations by keeping sensitive data within specific geographic regions, minimizing the risk of non-compliance.
  • Real-time Analytics at the Edge: Edge computing enables the application of real-time analytics and machine learning algorithms at the edge, providing actionable insights for optimizing industrial automation processes without the need for constant data transmission to a central server or cloud.

Emerson RXi Solutions

Emerson PACEdge was designed with seamless scaling and interconnectivity in mind. The Emerson edge solutions are one facet of this, allowing control engineers to easily move from pilot to enterprise implementation. Emerson solutions include:

  • RXi HMI, providing the visualization of the most important aspects of machine operation
  • RXi Edge Computing, collecting the process data to use machine learning processes to gain valuable insights and determine improvements
  • RX3i Edge Controller, providing output controls to smart devices and allowing rapid process optimization
  • RXi Edge Analytics, a pre-configured set of analytic engines that can easily visualize important data, including energy use, machine performance, and accomplishment of performance metrics
  • RXi Supervisor, providing a comprehensive view of production operations by connecting production data and plant assets.

Products such as those found in the PACSystems Edge portfolio enable control systems engineers to quickly develop, implement, and scale edge computing solutions that support IIoT and Industry 4.0.

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