We built an edge AI predictive maintenance platform that reads live sensor data from heavy machinery, forecasts component failures weeks ahead and schedules maintenance in quiet periods.
Industry: Manufacturing & EnergyService: AI & Automation
Overview
Client
Hitachi
Global industrial and technology group
Engagement snapshot
The challenge
Maintenance was reactive: parts were replaced after they failed, and each unexpected breakdown stopped production on the factory floor.
Sensors already produced high-frequency telemetry, but nobody could turn that stream into an early warning in time to act.
Failures halted production without warning.
High-frequency telemetry was collected but not acted on.
Maintenance often fell during busy production periods.
The solution
High-frequency sensor data from each machine is processed at the edge, next to the equipment.
Machine learning models predict component wear and imminent failures weeks in advance.
Maintenance tickets are raised and timed for low-demand periods, and operating parameters adjust to reduce strain.
Key features
Predictive models run on edge devices, close to the equipment and its live telemetry.
Business impact
Fast, local fault detection
Machine learning forecasts component wear and failures weeks before they happen.
Business impact
94% fault forecast accuracy
Tickets are raised automatically and timed for low-demand periods.
Business impact
Unplanned downtime eliminated at key sites
Before and after
Before
Fix after failure
Parts were replaced once they broke.
After
Fix before failure
Models forecast wear weeks ahead.
Before
Unplanned stops
Breakdowns halted production lines.
After
Planned windows
Work is scheduled when demand is low.
Before
Collected, not used
Telemetry was stored without early warnings.
After
Acted on at the edge
Live data drives forecasts and tickets.
Results
94% Forecast Accuracy
Models forecast equipment faults with 94% accuracy.
Before: ReactiveAfter: Predictive
Zero Unplanned Downtime
Unplanned downtime was eliminated across the key manufacturing facilities.
Before: BreakdownsAfter: Planned Windows
Weeks of Warning
Repairs are scheduled during low-demand periods instead of interrupting production.
Before: After FailureAfter: Before Failure
Testimonial
“We shifted our entire maintenance strategy from reactive to predictive. The edge AI models forecast equipment faults with 94% accuracy, completely eliminating unplanned downtime across our key manufacturing facilities.”
Shamik MehtaDirector, Industry Solutions, Hitachi Vantara, HitachiKey results
Trust and control
Maintenance teams review scheduled work before it goes ahead.
HUMAN IN CONTROLOperating adjustments stay within set limits.
CONTROLLEDForecast performance is monitored over time.
MEASUREDTelemetry is processed at the edge, close to the equipment.
EDGEFaster delivery
Building on tested components meant more time went into tuning the models to each machine type.
Tested components for high-frequency sensor data shortened the pipeline build.
A reusable edge runtime let models run beside the machines.
Ready-made workflows create and schedule maintenance tickets.
Forecast accuracy is tracked so models stay reliable.
We built an edge AI predictive maintenance platform for heavy industrial machinery. It takes in high-frequency telemetry from equipment sensors, forecasts component wear and imminent failures with machine learning, raises maintenance tickets automatically and adjusts operating parameters to keep machines running until the planned repair.
The edge models forecast equipment faults with 94% accuracy, weeks in advance. That early warning let maintenance move from reactive to predictive, and unplanned downtime was eliminated across the key manufacturing facilities, because repairs are scheduled during low-demand periods instead of waiting for a part to fail.
Running models on edge devices next to the machines means high-frequency sensor data is processed where it is produced, without sending every reading to the cloud. Faults are detected quickly and locally, and the platform can raise a maintenance ticket or adjust operating parameters before a small problem becomes a stoppage.
If breakdowns still halt your production lines, we can build edge AI that warns you weeks ahead.