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
1---2name: mfg-predictive-maintenance3description: "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'.".4---56# Predictive Maintenance78## Framework910```11IRON LAW: Predictive > Preventive > Reactive (but each has its place)1213Reactive (fix after failure): cheapest per-event, most expensive in downtime14Preventive (fix on schedule): prevents some failures, causes unnecessary maintenance15Predictive (fix based on condition): lowest total cost, requires sensor investment1617Not ALL equipment justifies predictive maintenance. Apply to equipment where18unplanned downtime cost >> sensor investment cost.19```2021### Maintenance Strategy Comparison2223| Strategy | When to Maintain | Advantage | Disadvantage | Best For |24|----------|-----------------|-----------|-------------|----------|25| **Reactive** | After failure | Zero upfront cost | Max downtime, safety risk | Non-critical, cheap-to-replace equipment |26| **Preventive** | On schedule (time/cycles) | Predictable, simple | Over-maintenance (replacing parts that still work) | Equipment with known wear patterns |27| **Predictive** | Based on condition data | Minimize downtime AND maintenance cost | Requires sensors, data infrastructure, models | Critical, expensive, failure-has-cascading-effect equipment |2829### P-F Curve (Potential Failure → Functional Failure)3031```32Condition33 │34 │ ●─── P (Potential failure detected by sensor)35 │ ╲36 │ ╲ ← P-F Interval (time to act)37 │ ╲38 │ ● F (Functional failure — equipment stops)39 │40 └──────────────────── Time4142The P-F interval is your window of opportunity. Detect at P, schedule43repair before F. The longer the P-F interval, the more planning time.44```4546### Sensor Data Types4748| Data Type | What It Detects | Equipment |49|-----------|----------------|-----------|50| **Vibration** | Bearing wear, imbalance, misalignment | Rotating machinery (motors, pumps, turbines) |51| **Temperature** | Overheating, friction, electrical faults | Motors, transformers, bearings |52| **Current/Power** | Load changes, electrical degradation | Electric motors, drives |53| **Acoustic** | Leaks, cavitation, micro-cracks | Pressure systems, pipes, valves |54| **Oil analysis** | Wear particles, contamination | Gearboxes, hydraulic systems |5556### ML Models for RUL (Remaining Useful Life)5758| Approach | Method | Data Required |59|----------|--------|-------------|60| **Statistical** | Weibull distribution, exponential degradation | Historical failure times |61| **Classical ML** | Random Forest, Gradient Boosting on sensor features | Labeled run-to-failure datasets |62| **Deep Learning** | LSTM, 1D-CNN on raw sensor time series | Large volumes of sensor data |63| **Anomaly Detection** | Isolation Forest, Autoencoder | Normal operation data only (no failure labels needed) |6465### Implementation Steps6667**Phase 1: Select Equipment** (criticality analysis)68- Which equipment has highest downtime cost?69- Which has cascading failure effects?70- Prioritize: high cost × high frequency7172**Phase 2: Install Sensors**73- Match sensor type to failure mode (see table above)74- Establish data pipeline: sensor → edge/cloud → storage7576**Phase 3: Build Baseline**77- Collect 3-6 months of normal operation data78- Establish "healthy" patterns7980**Phase 4: Develop Models**81- Start simple: threshold-based alerts (vibration > X = warning)82- Graduate to ML models as data accumulates83- Anomaly detection if you have few/no failure examples8485**Phase 5: Operationalize**86- Integrate alerts into maintenance workflow (CMMS)87- Define response procedures for each alert level88- Measure: reduction in unplanned downtime, maintenance cost savings8990### ROI Calculation9192```93Annual Savings = (Unplanned downtime hours reduced × Downtime cost/hour)94 + (Preventive maintenance events avoided × Cost per event)95 - (Sensor + infrastructure + model development cost)96```9798## Output Format99100```markdown101# Predictive Maintenance Plan: {Equipment/Line}102103## Equipment Criticality104| Equipment | Downtime Cost/hr | Failure Frequency | Cascading? | Priority |105|-----------|-----------------|-------------------|-----------|---------|106| {name} | ${X} | {X/year} | Y/N | H/M/L |107108## Sensor Plan109| Equipment | Failure Mode | Sensor Type | P-F Interval |110|-----------|-------------|-------------|-------------|111| {name} | {mode} | {sensor} | {est. hours/days} |112113## Projected ROI114| Metric | Before | After | Savings |115|--------|--------|-------|---------|116| Unplanned downtime | {hrs/year} | {hrs/year} | ${X}/year |117| Maintenance cost | ${X}/year | ${X}/year | ${X}/year |118| Sensor investment | — | ${X} one-time | Payback: {months} |119```120121## Gotchas122123- **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.124- **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.125- **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.126- **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.127128## References129130- For sensor selection guide by equipment type, see `references/sensor-guide.md`131- For LSTM-based RUL model tutorial, see `references/rul-tutorial.md`
charlieviettq