Play 68 — Predictive Maintenance AI
Industrial predictive maintenance — IoT sensor telemetry (vibration, temperature, pressure, current), multivariate feature engineering, Gradient Boosting RUL prediction, condition-based scheduling (urgent/planned/monitor), LLM root cause analysis with parts + repair time, failure pattern recognition, and analyst feedback loop for model improvement.
Architecture
| Component | Azure Service | Purpose |
|---|---|---|
| Sensor Ingestion | Azure IoT Hub | Vibration, temperature, pressure, current |
| Time-Series Store | Azure Data Explorer | 90-day telemetry archive, feature queries |
| RUL Model | Azure ML + scikit-learn | Remaining Useful Life prediction |
| Root Cause | Azure OpenAI (GPT-4o) | Failure mode explanation + action + parts |
| Scheduler | Custom | Condition-based work order generation |
| Prediction API | Azure Container Apps | RUL endpoint + scheduling |
How It Differs from Related Plays
| Aspect | Play 58 (Digital Twin) | Play 68 (Predictive Maintenance) |
|---|---|---|
| Focus | Full twin representation | Failure prediction specifically |
| Model | DTDL twin graph | ML regression (GradientBoosting) |
| Output | NL query results | RUL days + work orders + root cause |
| Features | Twin properties | Multivariate sensor stats (kurtosis, trends) |
| Scheduling | N/A | Condition-based: urgent/planned/monitor |
| Feedback | N/A | Analyst confirms→model retrains |
Key Metrics
| Metric | Target | Description |
|---|---|---|
| RUL MAE | < 5 days | Prediction error margin |
| Critical Detection | > 95% | Failures within 7 days correctly flagged |
| False Alarm Rate | < 10% | Healthy equipment incorrectly flagged |
| Downtime Reduction | > 40% | vs reactive maintenance |
| ROI | > 10x | Value delivered / system cost |
Cost Estimate
| Service | Dev | Prod | Enterprise |
|---|---|---|---|
| Azure IoT Hub | $0 | $25 | $2,500 |
| Azure OpenAI | $25 | $200 | $800 |
| Azure Machine Learning | $15 | $150 | $500 |
| Stream Analytics | $80 | $240 | $960 |
| Cosmos DB | $3 | $60 | $240 |
| Container Apps | $10 | $100 | $280 |
| Key Vault | $1 | $3 | $10 |
| Application Insights | $0 | $30 | $100 |
| Total | $134/mo | $808/mo | $5,390/mo |
Estimates based on Azure retail pricing. Actual costs vary by region, usage, and enterprise agreements.
WAF Alignment
| Pillar | Implementation |
|---|---|
| Reliability | Condition-based scheduling, multi-sensor correlation, feedback loop |
| Performance Efficiency | Multivariate features, cross-sensor correlation, batch predictions |
| Cost Optimization | Reduced unplanned downtime, right-time maintenance, parts pre-ordering |
| Operational Excellence | Work order generation, root cause analysis, quarterly model retrain |
| Security | IoT device authentication, Key Vault for credentials |
| Responsible AI | Explainable predictions with top indicators, human review for urgent |
FAI Manifest
| Field | Value |
|---|---|
| Play | 68-predictive-maintenance-ai |
| Version | 1.0.0 |
| Knowledge | T3-Production-Patterns, F1-GenAI-Foundations, O2-Agent-Coding, T1-Fine-Tuning-MLOps |
| WAF Pillars | reliability, cost-optimization, operational-excellence, performance-efficiency, security |
| Groundedness | ≥ 85% |
| Safety | 0 violations max |
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