Predictive Maintenance on AWS
Equipment failure prediction models trained on historical sensor telemetry — vibration, temperature, pressure, cycle counts — and served from AWS SageMaker. Alerts delivered before downtime occurs.
The Challenge
Industrial equipment failures are expensive. The direct costs — emergency repairs, replacement parts, unplanned downtime — are significant, but the indirect costs are often larger: lost production capacity, cascading schedule delays, and safety incidents that could have been avoided. Most organizations have years of sensor and maintenance history that has never been used for machine learning. The barrier isn't data availability — it's the infrastructure to move that data, build models on it, and get predictions into the hands of maintenance teams in time to act.
- • Unplanned failures drive disproportionate maintenance costs relative to planned replacements
- • Sensor telemetry from historians and SCADA systems is rarely in ML-ready format
- • Models built without production-grade retraining pipelines degrade as equipment ages
- • Maintenance teams need ranked, explainable alerts — not raw probability scores
- • Alert fatigue from binary threshold-based systems erodes trust in automated predictions
Our Solution
We build end-to-end predictive maintenance systems on AWS — from sensor data ingestion through model training, serving, and operational integration. The architecture connects your operational data to SageMaker, produces ranked equipment health scores, and delivers alerts through your existing maintenance workflow tooling.
- ✓ OT data ingestion via AWS IoT SiteWise and IoT Core with historian connectors for OSIsoft PI and Ignition
- ✓ S3 data lake with Glue ETL pipelines for validation, resampling, and feature engineering
- ✓ SageMaker Pipelines for model training, evaluation, and conditional registration with automatic rollback
- ✓ Batch inference producing ranked alert lists with SHAP-based feature explanations per asset
- ✓ Integration with CMMS and mobile maintenance workflow tools via API — no new interface required
- ✓ SageMaker Model Monitor with feature distribution tracking and drift-triggered retraining
Business Outcomes
- → Convert a meaningful fraction of unplanned failures into planned replacements — eliminating emergency procurement premiums and unplanned crew mobilization
- → Ranked alert lists that match how maintenance planning actually works — top N assets by failure probability, tunable to available crew capacity
- → SHAP-based explanations with each alert so maintenance teams understand why an asset is flagged and act with confidence
- → Model retraining triggered by data drift rather than calendar schedule — performance stays current as equipment ages and operating conditions change
- → Full ownership of code, pipelines, and runbooks — your team can extend and operate the platform after handoff
Getting Started
- 01 Identify two to three asset classes with documented failure history and good sensor coverage for the pilot
- 02 Map available data sources — historian systems, CMMS, sensor protocols — and assess data quality
- 03 Define operational KPIs with maintenance and operations teams before model training begins
- 04 Contact us to scope a focused assessment of your current data infrastructure
Ready to Get Started?
From predictive maintenance on grid infrastructure to renewable forecasting and upstream analytics, we scope engagements honestly and deliver systems your operations team can actually use.