THE BENEFITS OF IIoT FOR INDUSTRIAL EQUIPMENT SERVICE COMPANIES
Ever daydream about viewing, controlling and updating thousands of machines located across several continents from one simple dashboard? IIoT digitalization and smart data processing platforms have already taken this beyond the realm of the imaginary and into the possible. Smart-servicing and monitoring IIoT solutions start with apps where, through one or two simple settings, small crews of workers can be called to action and go all the way up to systems that monitor and manage a virtually unlimited number of large-scale machines in multiple locations. The benefits are equally scalable, and include less unplanned downtime, increased machine availability, decreased labor costs and improved work safety. Significantly, predictive maintenance can quickly translate into savings of between 8-12% costs over scheduled or preventative maintenance.
CONDITION MONITORING WITH IoT
Condition monitoring refers to inbuilt or retrofitted devices that monitor machine parameters and help to detect faults at an early stage or before they even appear. IIoT sensors, the cogs in the condition monitoring machine, can be used to measure anything from machine temperature to movement to adverse chemical reactions. Other servicing contracts may involve counting the number of people entering a room or calculating the air pressure in heating or AC systems. The readings are sent to the cloud, where an AI-backed middleware platform like relayr’s sorts the data and creates alerts or visualizes patterns on multiple devices carried or used by the relative monitoring or maintenance crews.
The key to predictive maintenance systems and asset monitoring, and the fastest growing IIoT application for industrial manufacturers, digital twin technologies create and visualize perfectly synchronized doppelgangers for machines or processes. Advancements in technology means these models are no longer simulations of the original objects, but true twins as they can also self-optimize according to real-time updates from their ‘partner’. Twin modeling not only indicates faulty parts or sends alerts to signal a current or coming malfunctions, it is also increasingly used to test planned software updates or changes to complex digitalized systems.
BUSINESS OUTCOMES FOR EQUIPMENT
According to the 2018 Gartner IIoT report, digital twin technologies will be implemented by half of all industrial companies by 2021. This is because business outcomes for predictive maintenance using digital twin technology can be significant, with an average efficiency gain of around 10%. Other significant outcomes include better data to aid future product development and improvement and more efficient processes on future lines. It also reduces material wastage and can make considerable time-saving in areas such as model building, planning, reporting and problem solving.