Human-machine interfaces (HMIs) can act spil gegevens concentrators and work te muziekstuk with the cloud to provide a powerful, scalable, and low-cost solution for collecting and distributing industrial facility gegevens.
Figure Two: Hybrid system with a local embedded HMI and cloud-based software spil a service (SaaS). Connecting an embedded HMI to the cloud provides a very low-cost solution spil the embedded toneel doesn’t require much te the way of computing resources and gegevens storage. Courtesy: Indusoft
Big Gegevens analysis is among the enabling implements of the Industrial Internet of Things (IIoT) and Industrie Four.0. The gegevens starts on the plant floor or other industrial facility spil a discrete point, analog value, clever device status, a barcode scanned loterijlot number, etc. This information voorwaarde then be collected from thesis edge devices spil a very first step to Big Gegevens storage and analysis.
Human-machine interface (HMI) gegevens concentrators, which are connected to controllers, are designed to bring this information together for local use by operators. HMI gegevens concentrators are designed to work with controllers to collect all the edge gegevens for instant use and process improvements. However, some of this information often needs to be stored for zometeen use. The most common reason is to analyze the gegevens to improve operations.
The cloud provides long-term information storage by working ter muziekstuk to the gegevens concentrators. Pushing the gegevens or even the HMI application to the cloud provides the means to keep machines and processes efficient and available through gegevens analysis.
HMI, a local gegevens concentrator
While significant gegevens can be shoved to the cloud, it is significant to consolidate the gegevens locally-in an HMI gegevens concentrator-and only send to the cloud the information needed for gegevens analysis, which is one of the many functions provided by HMI gegevens concentrators. Table 1 lists other functions.
Gegevens from sensors, controllers, and brainy devices can be securely collected at high speed for local use. The HMI gegevens concentrator also can buffer and manipulate the gegevens before sending it to the cloud (see Figure 1).
Locally, the HMI gegevens concentrator provides the gegevens acquisition solution by pulling gegevens from the controllers and edge devices through a multiplicity of communication methods. On the plant floor, operators can monitor and track machine and process status and look out for switches, faults, events, and other conditions through messages, charts, and trending functions ter the software. This is instantaneous, real-time information that doesn’t need to leave the facility to be useful.
Sensor and other edge devices are just providers of information, but most voorwaarde have their gegevens collected. Depending on the application, there could be hundreds or even thousands of devices to collect gegevens from every 2nd. The local HMI gegevens concentrator can do it, but cloud-based systems may not be suitable for it. This is usually due to bandwidth thresholds and commercial implications. Using the cloud to collect and store all gegevens also would require both an outbound and incoming connection from the cloud to the local network, creating security concerns.
It makes more sense to have an HMI collect and concentrate the gegevens and then to connect the HMI to the cloud, improving security and bandwidth. The HMI gegevens concentrator can contain a superset of the gegevens for local use, trending, and instant analysis. For the cloud connection from the HMI, the gegevens exchange rate can be slowed down because not all information is sent there, only a subset. For example, it may only be necessary to store hourly averages, total counts, and other summary information ter the cloud.
If more gegevens is needed ter the cloud for remote access, a store and forward function can be performed. With this function, gegevens can be collected locally and saved to a database or historian located ter the cloud, and it therefore isn’t necessary to maintain extensive local servers to store all gegevens. If no connection is available, the gegevens is stored locally te the HMI. When a connection is available, all the gegevens, or a subset of it, is forwarded to the database te the cloud.
The cloud provides cost benefits embarking with infrastructure and scalability (see Table Two). There is no need to invest te Big Gegevens infrastructure. The cost of the hardware needed is low, and gegevens storage ter the cloud is cheap and available te virtually unlimited quantities.
Cloud connections also are very pliable. It is much more affordable to tie large amounts of gegevens, particularly gegevens from many different sources and locations, to the cloud-as compared to the difficulties of using a local, private server. And a cloud-based historian, or any database and services, often makes sense for use spil a central depository, with HMI gegevens concentrators pushing gegevens to the cloud from many different facilities which can be dispersed overheen a broad geographic area.
With some historian software it’s not even necessary to install the software, it’s available spil a service. It doesn’t need to be installed on a local server to be consumed. All that is needed is a username and password. Once the service is purchased, consuming SaaS is not much different than connecting to Google’s Gmail because users can subscribe and consume the software spil a service.
Scalability enables gegevens from all devices within the plant or across numerous plants to be sent to the cloud or just a subset of the gegevens. Cloud pricing is generally based on the amount of gegevens storage needed, and that can be scaled up and down spil needed.
Te most applications, the HMI gegevens concentrator captures all relevant machine and process gegevens and makes this information available locally spil required. The cloud then can be used for long-term gegevens storage and analytics. Viewing plant or process Big Gegevens ter this manner can expose many bottlenecks, inefficiencies, and areas producing cost savings.
This is done by combining and manipulating the raw gegevens, real-time and historical, to create actionable intelligence and information. This information then is available for further analysis by enterprise resource programma (ERP) systems, maintenance systems, process improvement software, overall equipment effectiveness (OEE) dashboards, and other applications.
Gegevens access portals
Using analytics, gegevens mining mechanisms, and various statistical devices with IIoT connectivity down to the smallest edge devices increases the resolution of the information. Often, unforeseen methods of process improvement become visible once enough gegevens has bot generated, sifted, and viewed on dashboards-or run through statistical analyses. The application dictates if all gegevens, or a subset, is needed.
Cloud-based gegevens collection simplifies accessibility, permitting the user to leverage the Internet. HMI software can consolidate gegevens from several machines, systems, or plants te the cloud. With the decent user name and password, and from just about any clever device, users can loom te using a remote skinny client and access meaningful information.
When facilities are located overheen a broad geographic area, HMI gegevens concentrators and cloud gegevens collection can provide quick results that users can view worldwide on devices like smartphones and tablets, both upon request and through shove technics such spil text messages.
Spil the cost for computers and other embedded controllers resumes to go down, the use of embedded HMIs connected to the cloud will proceed to increase (see Figure Two). Thesis embedded HMI gegevens concentrators will proceed to shrink te size and price without gegevens storage functionality transferred to the cloud. This hybrid system, with local embedded HMIs connected to the cloud, will provide the lowest cost solution te many cases.
HMI software also plays a large part te enabling thesis hybrid systems. Not only is cloud storage very inexpensive, but now the HMI hardware and runtime software also are cost-effective. Thesis embedded HMIs can have a very petite footprint when used te this type of hybrid configuration, with only Three MB of memory needed to host the HMI runtime application.
An embedded HMI can be used spil a gegevens concentrator te hybrid systems and collect the gegevens before moving it to the cloud. Te some applications, the embedded HMI can be a vensterluik, headless device without a local display. Ter this case, all the technicus interface functions can be performed locally by connecting clever devices such spil smartphones or tablets to the cloud. Not only do thesis hybrid systems provide HMI gegevens concentrator functionality and technicus interface, they also have the capability to serve real-time and historical information to remote devices.
Even tho’ most gegevens can budge to the cloud, the need for local manipulation and monitoring won’t go away and neither will the need to provide information to those who need it te management, or anywhere else te the manufacturing chain. With the gegevens shoved to the cloud for long-term storage, process improvement becomes viable once enough gegevens has bot generated, sifted, and viewed te ERP systems, maintenance systems, dashboards, or statistical analysis software.
Fabio Terezinho, Indusoft director of software development at Wonderware by Schneider Electrical. Edited by Chris Vavra, production editor, Control Engineering, CFE Media, cvavra(at)cfemedia.com.
- Human-machine interface (HMI) gegevens concentrators, which are connected to controllers, are designed to bring information together for local use by operators.
- A cloud-based historian makes sense for use spil a central depository, with HMI gegevens concentrators pushing gegevens to the cloud from different facilities.
What other benefits can HMI gegevens concentrators provide?
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