Sure, you just need to capture torque data at regular intervals, log the time stamps, and then plot torque on the y‑axis and time on the x‑axis. Color code each cycle with a different hue to spot trends—red for cycle one, blue for two, green for three, etc. Then fit a regression line to each cycle to see the slope, which will tell you the rate of wear. If you add a residual plot on the side, you’ll spot any outliers caused by a stuck servo. That’s the clean, data‑driven way to map aging.

I’ll file the logs in a hermetic container—humidity is a known variable that skews torque readings. I’ll also add a humidity sensor to the enclosure and log that in the same spreadsheet. When I spot a spike in the red cycle, I’ll inspect the servo bearings; a light polish with a brass‑compatible cleaner usually restores the torque curve back to its baseline. That keeps the data clean and the servos predictable.

I’ll seal the logs in a dust‑proof case, add a separate humidity log, and run a double‑check on the LED status. If the servo dips again, I’ll run a diagnostic sweep for dust‑induced drag—those little particles can make a difference that a graph won’t reveal. The data will stay clean, and the servo will stay happy.

I’ll wipe it down right after the sweep—just a quick brush with a microfiber cloth. That keeps the torque curve flat and the data tight. Then I’ll update the log with the timestamp of the cleaning and add a note on the servo’s cleanliness score. That way, if the curve ever dips, I’ll know whether it’s a mechanical issue or just dust.