Predictive Maintenance

The problem

Reactive maintenance leads to unplanned downtime, while fixed schedules can replace parts too early. Teams need earlier, explainable warning signs drawn from the data their equipment already produces.

Approach

1. 01

   Sensor data ingestion

   Time-series readings such as vibration, temperature, and pressure are collected, resampled, and quality-checked.

2. 02

   Anomaly detection

   Models learn normal operating behavior per asset and flag deviations, accounting for operating mode and load.

3. 03

   Prioritized alerts

   Alerts include the sensors involved, a severity estimate, and recent history so technicians can act quickly.

4. 04

   Feedback loop

   Technicians confirm or dismiss alerts, and this feedback is used to tune thresholds and reduce false positives.

Architecture

• Streaming ingestion with buffering and validation
• Time-series storage and feature windows
• Per-asset anomaly models with versioning
• Alerting service with notification integrations
• Asset health dashboard

Evaluation methodology

How we would measure whether this system works — defined before it is built.

• Replay of historical sensor data with known events
• Precision and recall of alerts against labeled maintenance records
• Alert lead-time analysis
• Review of false positives with domain experts