Emerson Exchange 365

How can end users realize improved efficiency using the industrial internet of things (IIoT)? A practical first step is to select edge technologies supporting improved analytics, which form the basis for taking positive action to achieve business goals.

The latest generation of Emerson’s edge controllers, devices, and gateways make it easy to obtain the data, and to perform analytics at the edge or in the cloud. My article in InTech, "Better Performance Begins at the Edge," discusses the value of obtaining and analyzing edge-sourced data.

Discrete manufacturers and original equipment machine (OEM) builders are experts regarding their equipment, and they often use analytical methods for evaluating the performance of individual machines, a fleet of machines, or multiple sites. Using edge components to automate the gathering and analysis of data is key to successful efforts.

Supporting Analytics
Analytics projects begin by gathering and historizing vast amounts of raw data, but can only deliver good value when they evaluate the data to deliver useful information. These analytics activities may run on edge-located controllers, on site-located systems, in the cloud, or in any combination of these platforms.

Edge-Sourced Data
The necessary data comes from machinery and the plant floor, also known as the “edge” of automation. It can come from basic traditional sensors, more advanced smart devices like VFDs, and IIoT devices. Connections are made using hardwired I/O, wired networking, and wireless. Some data is not time sensitive, but often low latency is important. Getting edge-sourced data effectively requires the application of the right technologies in the right places.

The Value of Edge Processing
Cloud computing has been emphasized for many years and is good for aggregating many data sources. However, for many plant level analytical needs like predictive maintenance, it can be more effective to perform pre-processing and analytics right at automation edge where the data is generated. A new generation of edge-capable components makes this possible.

Edge Advantages
Some advantages of edge analytics are:

• Improved data privacy
• Better data fidelity and responsiveness
• Resilience against data transmission interruptions
• Reduced data communications
• Provides a better opportunity to “close the loop” for control

Integrating Edge Analytics
Programmable logic controllers have traditionally supplied much of the industrial edge data, but this is changing as more IIoT devices are being incorporated. Three leading edge components today are:

• Edge controllers: Perform real-time control combined with extensive data processing
• Edge devices: Perform local analytics, but not control
• Edge gateways: Transmit data, with little or no processing

Using Edge Analytics
Edge analytics are not an all-or-nothing proposition, so OEMs can begin in one area and improve as they learn through successful implementations. Edge gateways transmit information so equipment can be remotely monitored. Edge devices can do this as well, and can also combine many data sources to provide operators with information in a useful context.

Edge controllers can do it all. They are able to control the process, analyze available data, and actively adjust control based on the information created from analyses.

Start Analytics at the Edge
End users and OEMs are becoming aware of the large amount of untapped data at the edge, but it can be intimidating to kick off a project. Powerful edge components enable a practical approach to access this data and realize value from edge analytics.