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