Over the last 15 years, we've seen manufacturing undergo quite the transformation. Today, pretty much every process is being influenced by automation tools and data analytics. Doing this is helping companies ensure greater efficiency, cost reduction, and sustainability.
Managers know that data is crucial in all these, but harnessing it for smart decision-making is still an issue. This is because it requires adapting existing technology that was only used as protection gear and using it as a source of insights.
In this guide, we'll look at how different control gear, from circuit breakers to current transformers, can be used to enable analytics-driven decision making.
Control gear has always been the first line of defence in most factories. Normally, it involves the use of current transformers (CTs) to step down current, which then allows other equipment like breakers, relays, and starters to protect machines. If a motor is overloaded, the breaker trips, and if there's a current surge, the relay steps in. This setup works in ensuring safety but doesn't do more after that.
Manufacturing today needs more than just a simple reaction. Factories need to avoid downtime and unplanned failure, which can be quite costly in the production line.
This can be achieved through the use of data to set up what's referred to as intelligent protection. When a fault occurs, the industrial control systems should have enough data to tell operators why the fault occurred, how it happened, and whether it points to a deeper issue.
More than that, you'll also get data that shows things like trip patterns, which helps engineers predict and avoid future failures.
In traditional control systems, the focus is only on equipment health. But when you integrate data analytics, it expands to other business goals of the company.
These are:
Efficiency – Since you are monitoring all current, voltage, and switching behaviour, it's easy to know when there's energy waste. For example, if the consumption has gone high, you can always narrow it down to a specific machine.
Reliability – You won't have to just react anymore. Instead, you can take avoidance action, as you can always see early warning signs like irregular load patterns or frequent trips. This will mean that you'll avoid downtime as well as high repair costs.
Compliance – The law requires you to show proof that your equipment operates within safe parameters. Doing so is easy with control gear analytics, as you have the continuous data you collect from your breakers, relays, and sensors.
One of the best things about such an approach is that you don't need to rely on overall industrial data to know if you are operating as you'd like. The data here is specific, so it's easy to narrow down to a particular machine.
After collecting data, you now need to turn it into something more meaningful. So, instead of simply seeing that the breaker has tripped due to a faulty device, you can know if the root cause is poor load balancing in your system.
Here's a starting point:
Ensure you capture the right signals – Focus more on current, voltage, breaker trips, and switching events.
Clean up your data and organise it – start by taking out the parts you don't need. You can then structure it based on time and equipment.
Set the various thresholds for alerts – What counts as "normal", and what needs your attention?
Look for trends – Single events may not mean much, so look for patterns.Using Industrial Control Gear for Smart Analytics in Manufacturing They'll show what’s really happening in the background.
Connect signals to business outcomes – You can use the information to schedule maintenance, balance loads, or adjust your processes.
This will help you make smarter decisions and turn data into ROI.