Building Your Own IoT: Equipment Monitoring for New and Legacy Systems

PCB fabricators can pair IoT monitoring with AI to track equipment behavior, troubleshoot setups and build useful operating records.
by Sean Patterson

Walk through a printed circuit fabrication shop and ask how people know the equipment is running normally. Some machines have useful displays and records. Others depend on an operator checking a gauge, writing down a reading, or hearing something that doesn’t sound right. A lot of knowledge lives with the person standing next to the machine.

That works until a problem develops between checks or the person who usually catches it isn’t there. A drilling machine stops, a press needs attention and now production has to work around it. Later, someone asks when the problem started, and there may not be much of a record to check.

If you’ve priced a monitoring system, you know the costs can include hardware, installation, and ongoing software fees. Older equipment can add another expense when it needs custom work to connect.

A separate sensor and a small computer may give you a simpler place to start. AI can help your team identify what works best for your system and can help you work through the setup. This becomes a basic example of the Internet of Things, or IoT, when the monitoring device connects to a network so its readings can be collected or reviewed. For this article, we’re talking about watching equipment your shop already owns.

Working With the Equipment You Have

A PCB shop may have newer drilling equipment alongside a lamination press that has been in service for years. The press joins the layers of a multilayer board using heat and pressure. It may still do its job well without offering an easy way to collect and review its operating data.

Replacing useful equipment just to get better information is a hard expense to defend. And even a newer machine may not make the reading you need easy to find or compare over time.

The people operating and maintaining these machines can usually tell you where more information would help. One machine stops more often than the others. Another has started making an unfamiliar noise. Everyone knows about it, but nobody is sure when it changed or how often it happens. Ask them what they wish they knew sooner. They may be more concerned about repeated stops than a measurement you thought was important.

Depending on the equipment, a separate sensor can collect a measurement without connecting to the machine’s controller. That might be temperature, vibration, or electrical current. Vibration monitoring can require faster measurements and more careful sensor mounting than a simple temperature log. The right choice depends on the question you’re trying to answer and what can be measured reliably.

What You’re Building

A basic monitor collects a reading, saves it, and alerts someone when it needs attention. The sensor measures, the computer keeps the record, and a program handles the logging and alerts.

For a PCB shop, the question might be whether a machine’s motor is vibrating differently than usual, or how often equipment is running versus sitting idle. Those are different questions and may need different setups. If you use electrical current to estimate running and idle time, check those patterns against what the machine is actually doing. Start with the problem your team already spends time on.

Some installations need only a few inexpensive parts. Others require more protection or professional work. Price the whole job and compare it with an available monitor before deciding to build. The experience of building has value, but the installation still has to make sense.

Keep the monitor separate from the machine’s controls and safety systems. Qualified people should review the installation and handle electrical work. Include whoever manages your network and data before connecting the device or sending readings to an outside service.

Getting Help From AI

The programming is often where a small project gets stuck. Someone has to make the sensor readings reach a file, give them a timestamp, and arrange for an alert. AI can help write that program and explain the setup in language you can follow.

The key is to let the AI ask you questions before it proposes anything. Here is a prompt you can paste in and adapt to your own shop:

Have the appropriate people review what comes back. Your engineering team and equipment requirements still determine what readings are acceptable. AI doesn’t get to decide how your process should run.

Expect some back and forth. If the program fails, paste the error into the conversation and explain what you were doing. Try the correction and bring back the result. When it runs, compare the readings with a trusted measurement where practical. Check that timestamps and units are correct, that alerts arrive, and that a disconnected sensor or interrupted connection does not go unnoticed.

We see this in the cohorts we teach with people who have never written code make progress because they keep working through the questions. We remind people that code is simply text. They don’t understand every part on the first try, but they learn enough to take the next step and recognize when they need help.

Making Sense of the Readings

Once you’ve collected enough readings to understand normal operation, you can look for changes. Is the equipment stopping more often? Does unusual reading happen during the same part of a job? AI can help review the record and identify things worth checking.

Give it context about what was running and ask it to explain what the readings support. A change may reflect a different job or a planned adjustment. It may also deserve a closer look from maintenance. The data alone won’t always tell you which.

Compare the findings with production and maintenance records before deciding what to do. Equipment readings can help you investigate a problem, but they don’t replace the inspection and testing used to confirm board quality.

Over time, the record gives the team something more useful than a recollection that a machine “has been doing that lately.” You can see when the change appeared and what was happening around it.

Get the Team Involved

The team will use the information, respond to the alerts, and notice when a reading doesn’t match what’s happening on the floor. Whoever chooses what to monitor should understand what the reading means and what action it could lead to.

Fit the review into an existing production or maintenance discussion. If something needs follow-up, assign it to someone. Decide who receives alerts and what they’re expected to do with them. Otherwise, a useful warning can sit unread while everyone assumes somebody else is handling it.

Keep the first project small enough to finish. Get one installation working reliably and see whether the team finds it useful before adding more. It’s easy to start planning a screen for every department while the original machine still isn’t being monitored.

Think about the equipment failure that disrupted your last production schedule. What would you have wanted to know before it stopped the job? That question is a reasonable place to begin. A basic monitor may reveal changes worth investigating, but it will not necessarily predict a failure or explain its cause.

You can learn a great deal from one useful installation. Your team gets a better record of the equipment’s behavior and some experience building a tool for its own work. That is the point we’ve kept coming back to in this series: learn how to use AI on a problem close enough to the work that you can judge whether it helped.End of article content

Sean Patterson is an accomplished executive with extensive C-suite experience across CRO, COO, and CTO roles who now specializes in humanizing artificial intelligence implementation in business environments, particularly manufacturing; sean@crossgen-ai.com.

Patterson’s unique approach to AI implementation stems from his multifaceted leadership experience in the PCB industry, including serving as COO and CTO & head of AI at Summit Interconnect, various senior positions at TTM Technologies, and CRO of Nano Dimension. He built Amazon’s tractor trailer division and healthcare platforms. He currently serves as COO of StartGuides, providing military technology working backwards from the soldier. He is also on several nonprofit AI advisory boards in education.

Patteson brings practical insights into how PCB manufacturers can approach AI adoption strategically. His methodology emphasizes cultural adoption from the top, employee empowerment, and then automation. His approach to AI implementation is captured in his often-quoted principle: “AI adoption is not something a leader can delegate.”

Patterson holds a master’s in nuclear science and engineering from MIT and a bachelor’s in systems engineering with a focus on robotics from the United States Naval Academy.