The proliferation of Industrial Internet of Things (IIoT) devices and accompanying software platforms in industrial settings means there’s never been so much data available for manufacturers to tap into and work with. This abundance is both a blessing and a curse. Scores of sensors are constantly monitoring and reporting on the condition of factories and the key pieces of equipment within them. As a result, manufacturers have access to data around everything from temperature and humidity to motion, proximity and other details. How best to make sense of this flood of data points?
Viewing the data in some type of spreadsheet or other 2D representation is not ideal, for several reasons, not the least of which is that it’s difficult to immediately home in on the information that’s most important or relevant, and it’s difficult to navigate. Artificial intelligence (AI) and machine learning help bring clarity to the proceedings by running algorithms against the data to spot trends, pull out important information or otherwise help users better see the forest through the trees. Visualization builds upon this foundation by providing a different way to view and consume these insights.
3D Assets Meet Real-Time Data
Marrying real-time Internet of Things (IoT) data with existing 3D models is a powerful way to present the constantly updating flow of information from IoT sensors with additional visual context. Most medium-to-large manufacturing facilities—and a few of the more advanced smaller ones as well—already have a rich array of 3D assets at their disposal. The building in which they operate was designed in 3D, and the equipment that makes up their production line—the CNC machines, 3D printers, measurement and inspection devices, and so on—is captured in a 3D digital model. A comprehensive digital factory model further includes robots, assembly lines and automated conveyor systems.
Having all this rich 3D data already available provides an opportunity to then overlay it with IoT data and create a more visual experience for manufacturers when they’re consuming their IoT data, helping to guide their decisions. For example, by monitoring real-time data and alerts that are overlaid onto digital models, manufacturers can proactively repair their critical machinery before failure occurs. This predictive maintenance capability allows manufacturers to optimize maintenance and repair schedules, while preventing the downtime that could cause operations to come to a grinding halt.
Need to quickly figure out the humidity level or analyze its current trend in the middle of a factory, right where a moisture-sensitive piece of equipment is located? Again, a digital model provides a way to see the real-time data being piped in from the IoT sensor at the actual facility.
This visual context also allows users to better “read” proximity and motion data to understand how serious a potential issue might be. Picture, for instance, a motion sensor associated with a robotic arm, an overhead crane or some other piece of machinery with a specific path or range of motion. If the data from a motion or proximity sensor shows that the machine has slightly shifted or deviated from its path, viewing it in a 3D context can provide a better sense of whether or not it’s a real issue.
A Step Further
It’s not difficult to imagine taking this same capability—the ability to bring together real-time IoT data and 3D models—and merging it with augmented reality (AR) technologies and positional information to create even more in-depth experiences on the shop floor and in manufacturing.
Such a combination of technologies might result in a world in which a user can walk up to a piece of equipment within a factory, carrying a mobile device or wearing some type of head-mounted display, and be presented with real-time IoT data from a sensor on the machine, based on that user’s specific location within the facility. Via his or her AR device, the user could view a virtual CAD object of the machine, overlaid with IoT sensor data—anything from job status or tool speed to the machine’s internal temperature. All of this real-time information would be readily accessible without a user having to actually navigate a computer system at all.
The challenge that manufacturers face in successfully wrapping their heads around the piles of data that their IoT sensors are generating is significant. But finding ways to combine big-data insights with 3D digital models helps to clear a path forward, giving manufacturers one more way to focus on the data that matters most and improve decision-making across all aspects of their operations.
Jonathan Girroir is a technology evangelist and senior manager at Tech Soft 3D. Check out the company’s IoT demo to see recent innovations around displaying real-time IoT data in 3D, or view a demo on YouTube.