Hands-on reference implementation · Databricks
Machine Health & Predictive Maintenance
A Databricks machine-health implementation that converts simulated sensor telemetry into anomaly signals, machine risk and prioritized maintenance actions.
The problem
What this project is solving
Industrial teams need a way to distinguish ordinary sensor variation from machines that warrant attention. The useful result is not an anomaly chart by itself; it is a prioritized maintenance view that operations can act on.
Implementation
What I built
- Generated 48 hours of simulated sensor data across 20 machines, including temperature, vibration, pressure and runtime.
- Processed the data through Bronze and Silver layers with validation and cleaning.
- Applied anomaly logic and machine-level aggregation to produce risk scoring.
- Published a Gold layer and SQL dashboard for machines requiring attention and maintenance risk.
Reference implementation using simulated sensor data; not presented as an industrial client deployment.
Evidence
Reference results
Delivery perspective
Why this matters
The implementation links data engineering to an operational decision. Rather than stopping at telemetry, it turns the data into a machine-level attention list that could support maintenance planning.