mfg-predictive-maintenance

Business

Design predictive maintenance strategies using sensor data, ML models for remaining useful life (RUL), and the P-F curve framework. Use this skill when the user needs to reduce unplanned downtime, transition from reactive to predictive maintenance, evaluate sensor/IoT investments, or estimate equipment failure probability — even if they say 'machines keep breaking down', 'when will this equipment fail', 'should we invest in IoT sensors', or 'reduce unplanned downtime'.

QUICK START

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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