Thanks to in-building connectivity and Internet of Things (IoT) tools, factories have become smarter. It’s not odd to see industrial robots roaming factory floors and IoT tools can monitor how materials, components and finished products flow in real time. However, there’s still more that IoT can do for factories — including increase predictive maintenance, which is the practice of technicians attending to connected machines before they fail, according to Thomas, a manufacturing advertising platform and digital marketing service provider.
With industrial IoT (IIoT), and sitewide in-building connectivity, factories can boost their efficiency, decrease downtime and ensure their facility is consistently in motion. Before factories had this type of tech at their disposal, workers would have to spot a problem on their own and hope they could find a solution and make the repairs before serious damage was done.
Today such problems no longer exist. Those same factory workers can get an alert on their phone that a piece of equipment is not functioning normally. They can get an instant diagnosis, saving time that normally would be spent figuring out how to resolve the issue. Workers can leverage this technology to fix the issue before it become a crisis rather than hoping they’ll be able to.
How to implement predictive maintenance
Allowing companies to get ahead of an issue and address it before it begins is perhaps the biggest benefit predictive maintenance brings. The combination of low-latency connections, automation and the IoT equipment most operators already have in place make this maintenance possible. When factories use machine learning and predictive analytics to find patterns in their present and past data, they can spot warning signs and act immediately.
People can spot these warnings signs, too, but probably not as reliably, or as timely, as connected machines can. For example, a fleet driver might miss that their vehicle’s check-engine light is on. With IoT tech, the vehicle’s diagnostics system would report the problem immediately as well as explain when and how it should be addressed.
Here are four things Thomas recommends as factories embrace predictive capabilities:
Minimize manual data collection
Connected sensors and smart tools make it easier to spot potential issues faster and more reliably than manual equipment inspections. There will always be problems that require in-person diagnostic work, but factories should take advantage of the fact that initial data collection can be done offsite and automatically. This gives workers more time to focus on tasks that only a person can do.
Get used to working with data
The information machines can deliver can be overwhelming; Thomas recommends using an analytics platform that can process and learn from that data in order to “connect the dots between incoming performance metrics and required maintenance.” Workers then can make judgments on the analyzed information rather than trying to figure out the raw data.
Make full integration the target
The goal of IIoT is to generate maximum in-building connectivity. Each process and piece of equipment has to communicate seamlessly across the value chain for that to happen, however. Software and hardware alike should be integrated across all tools if a factory expects to have one unified data network that its predictive maintenance solution can use.
Integration can be difficult at times because various vendors’ products have to seamlessly work together. Factories need to make sure that every component in their infrastructure can collaborate with one another.
Deploy a cellular wireless network
Hardwired and Wi-Fi networks can connect IoT devices in some settings, but cellular is essential in many others. Cellular stands a better chance of keeping IoT solutions connected regardless of their conditions or locations. For example, a company drilling for oil needs SIM-based connectivity for its predictive maintenance system to accurately monitor the health of its extraction machine’s drill head and prevent any damage.
“After all, the machinery that is the hardest for field teams to access is what most urgently needs predictive maintenance capabilities,” Thomas wrote.
Factories that can keep their machinery connected and leverage collected data efficiently likely will get the most out of predictive maintenance, which can help lower costs and keep their facilities running at full capacity.