The idea that the Internet of Things (IoT) and the Industrial Internet of Things (IIoT) are fads is quickly fading. According to recent research from GE and Accenture, 73 percent of companies are already investing more than 20 percent of their overall technology budget on Big Data analytics. Moreover, three-fourths of executives expect that spending level to increase in the next year. With that amount of money being spent on IIoT, it’s no longer a question of if or when the IIoT will become reality. It is a reality today.
The real challenge is how to implement a solution today that can adjust to the volatility of a rapidly changing market. The solution can be found by looking at some key IIoT applications of early adopters and the adjacent market of the consumer IoT. Whether it’s Apple’s iOS or Android or wearables or online machine condition monitoring, the smart grid, or smart machines within the IIoT, the answer lies in a platform-based design.
The following five IIoT trends show how to use a platform-based approach to address this challenge:
Flexibility of future design. No one can truly predict what the future of the IIoT might bring, yet it is still critical to be prepared for it. Clearly, traditional approaches will no longer be enough. A perfect example of this is the evolution of the electrical grid.
“The modern grid comes with new engineering challenges,” says Peter Haigh of National Grid UK. “In the United Kingdom, as renewable energy resources are being used to supplement fossil fuel production, power quality issues are surfacing. Combine this with the rapidly increasing demand for energy and the decommissioning of fossil fuel plants, and grid operators are finding that traditional measurement systems do not offer adequate coverage to handle these new challenges and manage the new risks the industry faces.”
To address this challenge, National Grid UK adopted a platform based on NI’s CompactRIO system that can provide more measurements and adapt with the evolving grid for generations to come. Previously restricted to 20th-order harmonics, National Grid UK can now look at 100th-order harmonics. This is a 400 percent improvement.
Distribution and systems management. Machine condition monitoring is a prime example of the IIoT in action today. Several factors have prevented companies from adopting and enjoying the benefits of a predictive maintenance strategy. These shortcomings are primarily associated with the traditional approaches used to implement predictive maintenance strategies and not with predictive maintenance itself.
The two traditional approaches are: a complete end-to-end automated solution that covers everything from the site survey to installation to remote monitoring; and a manual round-based solution that involves technicians and experts regularly visiting each asset to collect measurements and then returning home to perform the analysis. The problems in both cases are cost and scalability. Covering more assets with an end-to-end or black-box solution requires a lot bigger hardware investment that does not generate the ROI needed to apply the solution to less critical assets. The same can be said of the route-based solution, which cannot provide enough manpower to perform rounds on those assets.
Using a platform-based solution can significantly reduce the cost to improve the ROI of covering more assets. For example, one tier 1 energy provider found that its vibration specialists could spend 60 percent more of their time analyzing the data rather than collecting data when using this automated platform because they could cover many more assets across the country more efficiently. The company could use high-level software to manage all of its systems and present the data so that vibration specialists could appropriately analyze it.
Unification of disparate sensor types. As condition monitoring and smart grid systems become more distributed, the need to unify all of the measurements into a single platform becomes more apparent. Not much is gained if some measurements are automated while others are still route-based. A platform can economically bring all of these measurements together in a process called sensor fusion to eliminate the need for route-based measurements or disparate automated measurement systems.
A great example of this is the consumer IoT and the Apple Watch. Through this platform, different sensor types such as photodiodes for pulse, accelerometers and GPS are combined with a single software solution to create a better experience. Likewise, with both condition monitoring and the smart grid, disparate measurement types can be brought together through a platform and a single software solution. With a single platform, maintenance managers can combine previously disparate measurements like electromagnetic signature analysis, motor current signature analysis, or thermography with other more common measurements like voltage, current or acceleration.
Modularization of smart machines through communication. The IIoT has also seen adoption within the machine builder community and with the spread of the smart machine. These machines can monitor their own health, change algorithms on the fly and, most importantly, communicate that information back to the enterprise and other machines to promote more effective decision-making. Obviously, this advanced functionality also adds significant complexity.
To combat this challenge, key solutions have emerged to take advantage of a platform-based approach. A platform can simplify development, which helps to modularize the machine into distinct subsystems with tight communication protocols. This type of design is perfect for the ever-changing nature of the market. Design teams can work in parallel to reduce time to market and quickly iterate to add new and advanced functionality.
This approach is continually evolving in terms of communication. As the performance of these machines improves, the communication capabilities also need to improve. This cannot happen with today’s closed proprietary protocols. The protocols need to be based on standard Ethernet technologies. They also need to incorporate evolving standards to enable a more open and deterministic network that meets IIoT latency, determinism and bandwidth requirements while maximizing interoperability between industrial system providers and the consumer IoT. Organizations like the Industrial Internet Consortium document use cases and ensure interoperability, and IEEE has formed the Time Sensitive Network task group to evolve IEEE 802.1 to meet these requirements. There is much work to be done, but this is proving to be a clear trend for the future.
Simplification of system complexity. Finally, platforms enable the proliferation of the IIoT and the smart machine by simplifying the system complexity of each modularized subsystem similar to how a platform consolidates condition monitoring measurements. Kennes Wang of Master Machinery used a platform-based approach when creating a smart semiconductor manufacturing machine and found that by using the new CompactRIO controller in the semiconductor pick-and-place machine, they were able to integrate local HMI and vision components into one device. Wang said that this “reduced not only our system costs but also our development time.”
The future of the IoT and IIoT relies on platforms because of their scalability and ability to integrate new technology. With the rapid pace of change, it is no longer feasible to build everything from scratch. Those who try will be left behind. A smarter approach is to build on a platform that helps users from different companies and industries across the globe leverage and contribute to the platform—a platform that moves everyone forward.
To learn more about the benefits of NI’s platform-based approach, visit ni.com/embedded-systems.
