By Grant Gerke, Contributing Editor
The Internet of Things (IoT) presents an interesting picture with regard to the consumer and industrial playing fields. One could be described as limping, compared with the other that’s maturing fast.
Industrial manufacturing, due to a focus on equipment and networking standards for the past 20 years, is seeing the fruits of its labors through increasing use of predictive analytics. One trend emerging in 2017 is the implementation of smaller IIoT (Industrial Internet of Things) pilot projects that can provide quick results. HIROTEC, a Tier-One supplier to global automotive companies, is an example.
Justin Hester, senior researcher in the IoT Lab for HIROTEC Corp., Hiroshima (hirotec.co.jp, hirotecamerica.com), described his company’s pilot IIoT implementation in a Mar. 2017 National Association Manufacturing (NAM) webinar. “Another way to look at it is I want to crawl, walk, and then run,” he said. “I want to start visualizing data and don’t even want to do augmented reality or predictive analytics yet. That’s where I need to go, but the company can’t make that jump from no IoT solution to a full, augmented reality and predictive analytics tomorrow.”
For the SCRUM project, the automotive supplier and machine-tool maker started with eight CNC-based machines at its Detroit facility in 2015.
The project involved the use of Kepware KEPServerEX from PTC (Needham, MA, ptc.com) and OPC UA network solution to move variable CNC-machine control data to the ThingWorx (Exton, PA, thingworx.com) IoT platform to produce real-time data analytics for executives, plant managers, and technicians. Hester noted that the control platforms and data types varied greatly at this business unit, which was one of the reasons for the pilot project.
“Project flexibility is needed, especially when we’re talking about IIoT technology,” stated Hester. “If you have this long lead time with a pilot-project implementation, you learn what matters with the organization. If you have a long, two-year project, you’re stuck.”
HIROTEC’s application is quite common in industrial manufacturing, and one of the drivers for success is the OPC UA networking platform. This technology allows manufacturers to hold on to legacy equipment and use data from those machines, something that keeps CapEx costs down.
As more interoperability data solutions mature, the space is seeing more entrants, including, recently, Silicon Valley-based called Element Analytics Inc., San Francisco (elementanalytics.com). This company is targeting predictive analytics for the continuous-process industries. In Jan. 2017, it formally unveiled its Element Platform. The platform takes unstructured, operational sensor data from production “silos” to a cloud-based system, where asset models help predict equipment downtime.
Utilities (specifically wastewater operations) represent yet another segment that seems ripe for IIoT applications.
According to Jim Gillespie, co-founder of Gray Matter Systems, Pittsburgh (graymattersystems.com), a new Software-as-a-Service (SaaS) tool called ClariFind will alert utilities on sludge overflow failures and also predict thickening failures related to effluent not settling correctly.
How do such operations pay for new sensing technology, equipment, and better communication infrastructure?
In a recent blog post on TechCrunch (techcrunch.com), Gillespie pointed to “utilities selling solutions to other wastewater operations as the power industry has done.” As an example, he described how the Washington D.C. Water and Sewer Authority recently commercialized its intellectual property and provided a new revenue channel in the process.
Such reports are snapshots of industrial trends in IIoT and predictive analytics. More success stories are coming. Maybe consumer players will take note. MT