Harvesting, storing, and accessing industrial data

Human-machine interfaces (HMIs) can act as data concentrators and work in concert with the cloud to provide a powerful, scalable, and low-cost solution for collecting and distributing industrial facility data.


Figure 1: PC-based HMI data concentrator with data collection in the cloud. PC-based HMI software can be used as a data concentrator, collecting information from edge devices and controllers installed in industrial facilities. Courtesy: IndusoftBig Data analysis is among the enabling tools of the Industrial Internet of Things (IIoT) and Industrie 4.0. The data starts on the plant floor or other industrial facility as a discrete point, analog value, smart device status, a barcode scanned lot number, etc. This information must then be collected from these edge devices as a first step to Big Data storage and analysis.

Human-machine interface (HMI) data concentrators, which are connected to controllers, are designed to bring this information together for local use by operators. HMI data concentrators are designed to work with controllers to collect all the edge data for immediate use and process improvements. However, some of this information often needs to be stored for later use. The most common reason is to analyze the data to improve operations.

The cloud provides long-term information storage by working in concert to the data concentrators. Pushing the data or even the HMI application to the cloud provides the means to keep machines and processes efficient and available through data analysis. 

HMI, a local data concentrator

While significant data can be pushed to the cloud, it is important to consolidate the data locally-in an HMI data concentrator-and only send to the cloud the information needed for data analysis, which is one of the many functions provided by HMI data concentrators. Table 1 lists other functions. 

Data from sensors, controllers, and smart devices can be securely collected at high speed for local use. The HMI data concentrator also can buffer and manipulate the data before sending it to the cloud (see Figure 1).

Locally, the HMI data concentrator provides the data acquisition solution by pulling data from the controllers and edge devices through a variety of communication methods. On the plant floor, operators can monitor and track machine and process status and look out for changes, faults, events, and other conditions through messages, charts, and trending functions in the software. This is immediate, 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 must have their data collected. Depending on the application, there could be hundreds or even thousands of devices to collect data from every second. The local HMI data concentrator can do it, but cloud-based systems may not be suitable for it. This is usually due to bandwidth limits and commercial implications. Using the cloud to collect and store all data 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 data and then to connect the HMI to the cloud, improving security and bandwidth. The HMI data concentrator can contain a superset of the data for local use, trending, and instant analysis. For the cloud connection from the HMI, the data 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 in the cloud.

If more data is needed in the cloud for remote access, a store and forward function can be performed. With this function, data can be collected locally and saved to a database or historian located in the cloud, and it therefore isn't necessary to maintain extensive local servers to store all data. If no connection is available, the data is stored locally in the HMI. When a connection is available, all the data, or a subset of it, is forwarded to the database in the cloud. 

Cloud advantages

The cloud provides cost benefits starting with infrastructure and scalability (see Table 2). There is no need to invest in Big Data infrastructure. The cost of the hardware needed is low, and data storage in the cloud is cheap and available in virtually unlimited quantities. 

Cloud connections also are highly flexible. It is much more affordable to tie large amounts of data, particularly data 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 as a central depository, with HMI data concentrators pushing data to the cloud from many different facilities which can be dispersed over a wide geographic area.

With some historian software it's not even necessary to install the software; it's available as 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 as a service.

Scalability enables data from all devices within the plant or across multiple plants to be sent to the cloud or just a subset of the data. Cloud pricing is generally based on the amount of data storage needed, and that can be scaled up and down as needed.

In most applications, the HMI data concentrator captures all relevant machine and process data and makes this information available locally as required. The cloud then can be used for long-term data storage and analytics. Viewing plant or process Big Data in this manner can reveal many bottlenecks, inefficiencies, and areas producing cost savings.

This is done by combining and manipulating the raw data, real-time and historical, to create actionable intelligence and information. This information then is available for further analysis by enterprise resource planning (ERP) systems, maintenance systems, process improvement software, overall equipment effectiveness (OEE) dashboards, and other applications.

Figure 2: Hybrid system with a local embedded HMI and cloud-based software as a service (SaaS). Connecting an embedded HMI to the cloud provides a very low-cost solution as the embedded platform doesn’t require much in the way of computing resources and dData access portals

Using analytics, data mining techniques, and various statistical tools 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 data has been generated, sifted, and viewed on dashboards-or run through statistical analyses. The application dictates if all data, or a subset, is needed.

Cloud-based data collection simplifies accessibility, allowing the user to leverage the Internet. HMI software can consolidate data from several machines, systems, or plants in the cloud. With the proper user name and password, and from just about any smart device, users can log in using a remote thin client and access meaningful information.

When facilities are located over a wide geographic area, HMI data concentrators and cloud data collection can provide quick results that users can view worldwide on devices like smartphones and tablets, both upon request and through push techniques such as text messages. 

Hybrid system

As the cost for computers and other embedded controllers continues to go down, the use of embedded HMIs connected to the cloud will continue to increase (see Figure 2). These embedded HMI data concentrators will continue to shrink in size and price without data storage functionality transferred to the cloud. This hybrid system, with local embedded HMIs connected to the cloud, will provide the lowest cost solution in many cases.

HMI software also plays a large part in enabling these hybrid systems. Not only is cloud storage very inexpensive, but now the HMI hardware and runtime software also are cost-effective. These embedded HMIs can have a very small footprint when used in this type of hybrid configuration, with only 3 MB of memory needed to host the HMI runtime application.

An embedded HMI can be used as a data concentrator in hybrid systems and collect the data before moving it to the cloud. In some applications, the embedded HMI can be a blind, headless device without a local display. In this case, all the operator interface functions can be performed locally by connecting smart devices such as smartphones or tablets to the cloud. Not only do these hybrid systems provide HMI data concentrator functionality and operator interface, they also have the ability to serve real-time and historical information to remote devices.

Even though most data can move 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 in management, or anywhere else in the manufacturing chain. With the data pushed to the cloud for long-term storage, process improvement becomes viable once enough data has been generated, sifted, and viewed in ERP systems, maintenance systems, dashboards, or statistical analysis software.

Fabio Terezinho, Indusoft director of software development at Wonderware by Schneider Electric. Edited by Chris Vavra, production editor, Control Engineering, CFE Media, cvavra@cfemedia.com.


Key Concepts

  • Human-machine interface (HMI) data 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 as a central depository, with HMI data concentrators pushing data to the cloud from different facilities.  

Consider This

What other benefits can HMI data concentrators provide?

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