Mfg Predictive Maintenance Agent Skill

Predictive Maintenance

Framework

IRON LAW: Predictive > Preventive > Reactive (but each has its place)

Reactive (fix after failure): cheapest per-event, most expensive in downtime
Preventive (fix on schedule): prevents some failures, causes unnecessary maintenance
Predictive (fix based on condition): lowest total cost, requires sensor investment

Not ALL equipment justifies predictive maintenance. Apply to equipment where
unplanned downtime cost >> sensor investment cost.

Maintenance Strategy Comparison

| Strategy | When to Maintain | Advantage | Disadvantage | Best For |

|----------|-----------------|-----------|-------------|----------|

| Reactive | After failure | Zero upfront cost | Max downtime, safety risk | Non-critical, cheap-to-replace equipment |

| Preventive | On schedule (time/cycles) | Predictable, simple | Over-maintenance (replacing parts that still work) | Equipment with known wear patterns |

| Predictive | Based on condition data | Minimize downtime AND maintenance cost | Requires sensors, data infrastructure, models | Critical, expensive, failure-has-cascading-effect equipment |

P-F Curve (Potential Failure → Functional Failure)

Condition
  │
  │  ●─── P (Potential failure detected by sensor)
  │     ╲
  │      ╲  ← P-F Interval (time to act)
  │       ╲
  │        ● F (Functional failure — equipment stops)
  │
  └──────────────────── Time

The P-F interval is your window of opportunity. Detect at P, schedule
repair before F. The longer the P-F interval, the more planning time.

Sensor Data Types

| Data Type | What It Detects | Equipment |

|-----------|----------------|-----------|

| Vibration | Bearing wear, imbalance, misalignment | Rotating machinery (motors, pumps, turbines) |

| Temperature | Overheating, friction, electrical faults | Motors, transformers, bearings |

| Current/Power | Load changes, electrical degradation | Electric motors, drives |

| Acoustic | Leaks, cavitation, micro-cracks | Pressure systems, pipes, valves |

| Oil analysis | Wear particles, contamination | Gearboxes, hydraulic systems |

ML Models for RUL (Remaining Useful Life)

| Approach | Method | Data Required |

|----------|--------|-------------|

| Statistical | Weibull distribution, exponential degradation | Historical failure times |

| Classical ML | Random Forest, Gradient Boosting on sensor features | Labeled run-to-failure datasets |

| Deep Learning | LSTM, 1D-CNN on raw sensor time series | Large volumes of sensor data |

| Anomaly Detection | Isolation Forest, Autoencoder | Normal operation data only (no failure labels needed) |

Implementation Steps

Phase 1: Select Equipment (criticality analysis)

• Which equipment has highest downtime cost?
• Which has cascading failure effects?
• Prioritize: high cost × high frequency

Phase 2: Install Sensors

• Match sensor type to failure mode (see table above)
• Establish data pipeline: sensor → edge/cloud → storage

Phase 3: Build Baseline

• Collect 3-6 months of normal operation data
• Establish "healthy" patterns

Phase 4: Develop Models

• Start simple: threshold-based alerts (vibration > X = warning)
• Graduate to ML models as data accumulates
• Anomaly detection if you have few/no failure examples

Phase 5: Operationalize

• Integrate alerts into maintenance workflow (CMMS)
• Define response procedures for each alert level
• Measure: reduction in unplanned downtime, maintenance cost savings

ROI Calculation

Annual Savings = (Unplanned downtime hours reduced × Downtime cost/hour)
               + (Preventive maintenance events avoided × Cost per event)
               - (Sensor + infrastructure + model development cost)

Output Format

# Predictive Maintenance Plan: {Equipment/Line}

## Equipment Criticality
| Equipment | Downtime Cost/hr | Failure Frequency | Cascading? | Priority |
|-----------|-----------------|-------------------|-----------|---------|
| {name} | ${X} | {X/year} | Y/N | H/M/L |

## Sensor Plan
| Equipment | Failure Mode | Sensor Type | P-F Interval |
|-----------|-------------|-------------|-------------|
| {name} | {mode} | {sensor} | {est. hours/days} |

## Projected ROI
| Metric | Before | After | Savings |
|--------|--------|-------|---------|
| Unplanned downtime | {hrs/year} | {hrs/year} | ${X}/year |
| Maintenance cost | ${X}/year | ${X}/year | ${X}/year |
| Sensor investment | — | ${X} one-time | Payback: {months} |

Gotchas

• Start with vibration monitoring: It's the most mature, best-understood predictive technique. 80% of rotating equipment failures can be predicted by vibration analysis alone.
• Data quality > model complexity: A simple threshold alert on clean sensor data outperforms a sophisticated ML model on noisy, incomplete data. Fix data quality first.
• False positives kill adoption: If the model cries wolf too often, maintenance teams ignore it. Tune for high precision (few false alarms) even at the cost of some missed detections early on.
• Cultural change is harder than technology: Shifting from "run to failure" culture requires management buy-in and maintenance team training. Technology alone won't change behavior.

References

• For sensor selection guide by equipment type, see references/sensor-guide.md
• For LSTM-based RUL model tutorial, see references/rul-tutorial.md