- Predictive maintenance replaces fixed-interval and run-to-failure maintenance with a per-asset risk score and a predicted failure window.
- Cisco builds models on H2O AI Managed Cloud and deploys them with H2O eScorer on the factory floor.
- H2O Driverless AI returns a reason code with every prediction, so a technician sees which sensor readings drove the score.
- H2O MLOps keeps a central model registry, watches for model drift across plants, and flags when a model needs retraining.
Your maintenance calendar does not know which machine is about to fail
You are measured on uptime, throughput and maintenance cost, and unplanned downtime hits all three at once. A fixed calendar replaces parts that still have life in them, and still misses the bearing that fails between inspections.
Your plants already collect the signal: sensor data in historians, failure history in the maintenance log. The work is joining them into a score your planners trust, then getting that score to a line whose network may be segmented or partly offline.
How predictive maintenance works
Step 1: Join the history
Combine sensor time series data with maintenance logs, failure records and operating conditions for each asset.
Step 2: Frame the prediction
Predict failure within a window, remaining useful life, or an anomaly flag when readings leave normal behavior.
Step 3: Build the model
H2O Driverless AI creates lag and rolling-window features, searches models and documents the result.
Step 4: Score where the equipment runs
Models can be scored outside the platform's own environment, including on the factory floor. Cisco builds its models on H2O AI Managed Cloud and deploys them with H2O eScorer on the factory floor.
Step 5: Explain and act
Reason codes show which readings drove each score; your planners turn high-risk assets into work orders.
Step 6: Monitor and retrain
H2O MLOps keeps a central model registry, watches for model drift across plants, and flags when a model needs retraining.
Keeping plant data where it belongs
Everything can run in any of four deployment options: on-premises, air-gapped, private cloud or cloud, so plant data that cannot leave a site stays on site.