Business Impact
- $5.1M annualized savings via avoided downtime.
- Mean time between failures (MTBF) improved 19%.
- Technician truck rolls reduced 23%.
Challenge
Reactive maintenance and fixed interval schedules failed to capture early degradation signals, causing costly outages and over‑servicing reliable equipment.
Solution
- High-frequency telemetry ingestion & quality normalization.
- Feature engineering (rolling stats, frequency transforms, health indices).
- Failure probability & remaining useful life (RUL) models.
- Risk-based work order prioritization integrating with CMMS.
- Model drift & data quality monitoring.
Architecture Highlights
- Time-series feature store with incremental updates.
- Containerized inference services w/ GPU acceleration where needed.
- Model registry & automated retraining triggers.
- Alerting pipeline with SLA thresholds.
Outcomes
Operational reliability improved while maintenance labor and parts usage were optimized; faster iteration accelerated onboarding of secondary asset classes.