The industrial IoT (IIoT) is a relatively new concept for many industrial companies and manufacturers, but FogHorn Systems has been leading the development and implementation of IIoT with a pioneering approach to edge computing and analytics since 2014. This means a lot of time spent with customers and end users, understanding their industrial domains and where inefficiencies lie, taking that feedback and applying it to edge computing. Here are three top insights FogHorn Systems’ team has found while deploying edge computing on factory floors, wellheads, mines, refineries, etc.
Finding the important data
An impossible amount of sensor data is produced every day by industrial machines. And as the industry continues to enter the IIoT arena, the amount of data will only increase. Often companies are unsure of what they will ultimately do with their data and how they will realize any value from it. To avoid the exponential costs that transferring all that data to the cloud and storing it can cause, summarizing this data into a metadata format (i.e. averages and data points during relevant events) can extract the key data and insights to be moved to the cloud at a fraction of the cost to transport and store.
Often operational managers prefer to leave their legacy processing systems alone, embodying the old saying “if it’s not broke, don’t fix it,” and many of those factories have localized, well-programmed control systems in place. Changing a control system in the tiniest way risks completely breaking the effectiveness of the system, which can lead to months of testing and re-testing, causing operators to not update the systems in place. Edge computing, however, has the ability to make real-time decisions locally or in very close proximity to the devices themselves with a platform that makes it easy to develop and iterate intelligence. In short, edge IIoT can work at the machine level as well as the system level and is both open and flexible, while legacy control systems aren’t.
Looking at the big picture in real-time
Many companies have made significant investments in control systems that look at individual types of machines. But a single process may use many different machines that speak different languages and have different control requirements. In this case, process optimization requires providing actionable insights on large amounts of disparate, but highly correlated data. Significant investment is made in data science services to try to serialize and piece this data together after the fact. Unfortunately, the opportunity to act on data that predicts an impending problem can expire in the blink of an eye. Edge intelligence can capture the complex interplay between disparate machines in real-time, ensuring issues are caught before they cost a company money.
While the notion of edge computing is still relatively new even to those in the IIoT community, industrial and manufacturing companies are making strides to utilize IIoT to improve their processes and minimize their costs. New efficiencies and use cases will surely emerge as IIoT continues to be developed and adopted on factory floors and beyond.
The article was written by Matthew C. King, IIoT Solutions Expert, FogHorn Systems.