The rapid growth of Industrial Internet of Things (IIoT) solutions is poised to transform a myriad of industry verticals, including automotive, logistics, industrial automation, and transportation. Not only will these industries harness the benefits of IIoT solutions to improve production and customer satisfaction, but will also drive operational improvements and efficiencies across their business.
In some cases, however, connecting IIoT devices can be difficult, especially if a firm is operating within a radio frequency (RF) restricted environment. EDF Energy, the largest producer of low-carbon electricity in the UK, was facing this exact challenge.
Because EDF’s nuclear power plants operate in a RF-restricted environment, it traditionally relies on a network of wired cables to communicate sensor readings. And the planning, installation, and maintenance of these cables represents a large portion of the cost of ownership of such a wired industrial network (Dujone, et al, 2005).
This network was not only costly, but it was not able to gain readings and communicate them in a timely manner. EDF needed not only a way to communicate this information wirelessly, but it also needed a way to harness the power of big data analytics for predictive maintenance, providing them with a more cost-effective way to understand where faults might arise to prevent outages.
In this case study, you’ll learn more about how EDF Energy and Chirp worked together with the help of the UK Government’s Innovate UK programme to create a custom solution to capture real-time sensor data and communicate it wirelessly using Chirp’s data-over-sound technology.