Predictive Maintenance from Test Data

Predictive Maintenance from Test Data in TofuPilot

Your test data already contains the signals for predictive maintenance. A test fixture that's wearing out doesn't fail suddenly. Its measurements drift first. TofuPilot's trending tools let you see the drift before it causes failures.

What Test Data Reveals About Equipment Health

Every test run is a health check on two things: the unit under test and the test equipment itself.

When a measurement starts drifting, it could be the product changing or the test station changing. Separating the two is the key to predictive maintenance.

The distinguishing factor: if the drift or failure is specific to one station, it's the equipment.

Using TofuPilot for Fixture Health Monitoring

Track Key Measurements by Station

Filter measurements by station to isolate equipment-related trends from product-related trends.

from tofupilot import TofuPilotClient

client = TofuPilotClient()

# Get recent runs from a specific station
runs = client.get_runs(
    procedure_id="BOARD-FUNCTIONAL",
    limit=500,
)

# Track contact resistance trend (proxy for pogo pin wear)
contact_readings = []
for run in runs:
    for step in run.get("steps", []):
        for m in step.get("measurements", []):
            if m["name"] == "contact_resistance_mohm":
                contact_readings.append({
                    "date": run["created_at"],
                    "value": m["value"],
                    "station": run.get("station_id"),
                })

# Rising contact resistance = pogo pins wearing out

Set Maintenance Thresholds

Don't wait for a fixture to fail. Set maintenance thresholds based on measurement trends.

When a station's contact resistance trend crosses the maintenance threshold, schedule pin replacement. Don't wait for it to hit the failure threshold.

Monitor Cycle Time as a Health Indicator

Test cycle time is an underused equipment health signal. A test that normally takes 42 seconds and starts taking 55 seconds is telling you something:

• Instrument communication delays (aging GPIB cables, USB hub issues)
• Actuator slowdown (pneumatic fixture, motorized probe)
• Retry loops (intermittent contacts causing measurement retries)
• Software issues (memory leaks in long-running test software)

TofuPilot tracks cycle time for every run. A sudden increase is an early warning.

Calibration Drift Detection

Test instruments drift over time. A DMM that reads 3.300V today might read 3.305V next month. If your spec limit is 3.35V, that 5mV drift eats into your margin.

TofuPilot helps detect calibration drift by comparing measurement distributions over time:

1. Establish a baseline distribution during the first month after calibration
2. Monitor the distribution monthly
3. If the mean shifts by more than 10% of the spec range, recalibrate

This approach lets you move from calendar-based calibration (every 6 months regardless) to condition-based calibration (when the data shows drift). You save money on unnecessary calibrations and avoid the risk of running with a drifted instrument.

From Reactive to Predictive

The data for predictive maintenance is already flowing through your test stations. TofuPilot stores it, trends it, and surfaces the early warning signs. You just need to look.

Building a Maintenance Dashboard

Create a monitoring view focused on equipment health:

1. Station FPY comparison: Drop in one station's yield = equipment issue
2. Contact resistance trend per station: Rising trend = fixture wear
3. Cycle time trend per station: Increasing trend = equipment slowdown
4. Measurement variance per station: Increasing variance = intermittent contacts

Review this dashboard weekly. Most equipment issues give you days or weeks of warning before they cause production impact. The data is there. TofuPilot makes it visible.