Can AI Find the Root Cause of my Robot Data issue?
Can we actually use AI to chase down these technical "ghosts" in our data? Here is what we found out when we put Gemini to the test with real PLC logs.
Putting AI to the Test: Finding the "Noise"
The real challenge was the noise on our smart robot’s A3 position. I exported the logs into a CSV and fed them into Gemini to see if it could identify the root cause.
Pattern Recognition. The AI was surprisingly quick to spot something I might have overlooked in a massive spreadsheet: the spikes were only occurring on even numbers. This kind of pattern recognition is where AI excels, turning a wall of numbers into a visible trend almost instantly.
The Limits of Logic. While the AI identified the pattern, its initial suggestion—a software buffer—didn't solve the problem. It suspected a data integrity issue during a synchronous copy (CPS) instruction. While it wasn't a "silver bullet" solution, it confirmed my suspicion that the data was being overwritten mid-stream.
The "Human" Element. The hardest part of troubleshooting is realizing when the data doesn't look like what you expect because something else is writing to it. AI can show you the what, but as technicians, we still have to figure out the why.
Key Takeaways for Technicians
Trust but Verify: AI is a powerful tool for finding patterns in logs, but it doesn't replace a solid understanding of PLC scan cycles and memory mapping.
UDT Awareness: When importing tags into Ignition, always check your UDT Definitions. Just because they don't show up in your root folder doesn't mean the import failed.
Clean Data is King: If your logs are messy, the AI's advice will be too. High-fidelity data is the only way to get high-fidelity answers.
We're going to use these findings to simulate noise in future training exercises, helping students learn to distinguish between electrical interference and data integrity flaws.