If you’re involved in any kind of Internet of Things (IoT), Industry 4.0, or advanced manufacturing initiative in general, a key task will involve extracting data from older equipment. Given that large numbers of automated equipment and devices across industry pre-date any sort of broad, network-centered data aggregation or standardization efforts, this task is not as straightforward as it might seem.
Luke Stephenson, business manager, Enterprise Automation.To better understand how manufacturers can more effectively go about extracting data from older equipment in a meaningful way, we connected with Luke Stephenson, business manager at Enterprise Automation, a control systems integrator, for a recent episode of the “Automation World Gets Your Questions Answered” podcast series.
“Everybody knows businesses that use, collect, and observe data in real time are more agile and more effective when it comes achieving their objectives,” said Stephenson.
But before you head out to collect all the data you can from your equipment, Stephenson said there are a many important questions to ask; the first of which should be: What would those equipment data, if they were available to you in real time, enable you to do?
Stephenson said this question is so important because the way in which data is stored, accessed, and gathered can “really change what you’re able to do with it.”
Collecting data is all well and good, but making use of the data is really why you invest the time and energy to do it. And today, the cloud is a safe way to do this—even though people sometimes shudder at the term ‘the cloud.
Offering an example to show why answering this question is so important to determining your data aggregation approach, Stephenson said: “If you have a factory floor with one hundred machines on it, you don’t go after [the data from] all one hundred machines right out of the gate.” He said you should start with one machine to learn which data collection methods work best for you so that you can then scale that approach.
However, if your company is aware of how it could use a particular range or data set from different equipment if they could just get that data into a repository, then have that target serve as a baseline from which to determine the best data extraction methods for your equipment and use that to scale up from.
Essentially, the determining point here is whether you know what data you need and for what purpose(s). If you’re uncertain, start with one machine.
Data format and communication difficulties
Explaining why older equipment can be more difficult to extract data from, Stephenson pointed to devices that communicate using “serial communication protocols and protocols that are often considered proprietary. Once you start getting into Ethernet-based communication or even just two-wire or four-wire communication, there are a lot more ways to pick that information up.”
Most legacy devices are not going to have a built-in ability to transmit on sort of modern IoT type of communication protocols or be able to communicate with those types of platforms directly, Stephenson said; so you have to install a translator between the legacy device and the edge-of-network device used to collect the data.
“You want to translate the data from the legacy protocol into a more modern version, such as MQTT, so that you can then use it in different ways,” he said.
Stephenson also cautioned users to pay attention to the format used for extracted data. Formats such as JSON (JavaScript Object Notation) will typically be more viable with IoT platforms than files in .csv or .xml formats.
The importance of MQTT and the cloud
“MQTT (message queueing telemetry transport) is going to be the most common and most supported data messaging service out there,” said Stephenson.
Essentially, when a system is built for data collection—whether it’s for use with older or more recent equipment—it’s most likely going to include the use of edge devices to get equipment data into a format that can be used for different purposes and by different systems.
“A publish and subscribe message broker, like those used for MQTT, are scalable and built for the future,” said Stephenson. “I can’t think of one IoT device that’s not capable of communicating via MQTT. And all software systems, whether they’re SCADA (supervisory control and data acquisition), CMMS (computerized maintenance management system) or ERP (enterprise resources planning), can connect to MQTT message brokers. They’re super-efficient, fairly easy to set up, and very scalable. And they’re effective whether you’re connecting multiple devices in a plant or multiple plants across the country.”
He also stressed the importance of aggregating the data you collect in the cloud.
“This is where the rubber hits the road. Collecting data is all well and good, but making use of the data is really why you invest the time and energy to do it. And today, the cloud is a safe way to do this—even though people sometimes shudder at the term ‘the cloud,’” said Stephenson.
He explained that the cloud is integral to data collection and analysis because once your data is in the cloud or other data repository, there are a host of services and software that can help you with it—whether that’s for data visualization through something like Microsoft’s Power BI or Tableau or for specific applications like predictive maintenance.
“These tools go beyond what humans can do in terms of drawing insightful conclusions. It’s also a good way to make the data available to those with credentialed access wherever they may be located. We want to be able to get our data when we need it; and putting it in the cloud does that the best,” Stephenson said.
