Silent wear
A contactor degrades for weeks. Nothing flags it, and nothing says what to do.
Asset intelligence
Predict EV charger failures up to 3 weeks in advance
Predictive and prescriptive maintenance that shows which chargers will fail, and what to do about it. Iris R-One ranks every charger by the asset health index, predicts which component will fail and when, and prescribes the fix as a remote action or a planned work order.
/Predictive Maintenance
Failure Risk
At riskScheduledCompleted
CP-9034Failure predicted
ComponentContactor
Likely failure in7 days
Prescribed actionReplace contactor on the next planned visit
- Up to 3 weeks
- Advance warning before a charger fails
- Up to 97%
- Prediction accuracy of predicted chargers actually failed
Most failures give weeks of warning. Few teams see it.
Wear builds quietly until a driver finds it.
Every charger looks equal
Without a health ranking, crews visit the wrong chargers first.
A prediction with no action
A risk alert that never becomes a job changes nothing.
Up to 3 weeks of warning, and the predictions hold.
R-One flags a likely failure early enough to plan the fix. Of the chargers it predicted would fail, up to 97% actually did.
Of 100 chargers R-One predicted would fail
Predictive maintenance case studies
How R-One ranked charger failure risk for two operators, on their own history.
Case studyEuropean charge point operator
Finding the 22% of chargers behind 69% of failures
A European charge point operator fixed chargers after they failed. R-One showed where failures concentrate, and which 40% of the estate could safely wait.
Read case studyCase studyIndian fleet operator
Covering 90% of failures by focusing on 60% of the chargers
At an Indian fleet charging hub, R-One ranked every DC charger by failure risk, and showed which 20% could safely be left alone.
Read case study
From risk to the right intervention.
Prediction says what will fail. Prescription says what to do about it.
Predictive and prescriptive maintenance in the web app
/Predictive Maintenance
Failure Risk
At riskScheduledCompleted
CP-9034Failure predicted
ComponentContactor
Likely failure in7 days
Prescribed actionReplace contactor on the next planned visit
Asset health index
One health measure per charger, from Excellent to Failed, ranked across the network.
Failure prediction
Component-level risk with confidence and expected timing, up to 3 weeks before failure.
Remaining useful life
Expected life per component based on duty cycle and history.
Prescribed action
The recommended fix, ranked by impact, attached to every prediction.
Remote fix or work order
The prescribed action runs remotely or becomes a condition-based job with the part reserved.
Spare parts forecast
Parts demand projected from predicted failures.
Predict, prescribe, act, learn.
Score
The asset health index ranks every charger from telemetry, faults and service history.
Predict
Component risk and expected timing are forecast, up to 3 weeks ahead.
Prescribe
The recommended action is attached to each prediction.
Act
A remote action runs, or a work order is created with the part reserved.
Learn
What the technician finds on site feeds back to the model.
In the mobile app
The prescribed fix arrives as a ready job.
The prescribed fix reaches the technician as a job with the part reserved, and what they find on site feeds back to the model.
- The job opens with the predicted component and its risk
- The reserved part is listed before dispatch
- Site findings confirm or correct the prediction
Connected to the lifecycle.
Asset data, fault history and completed work train the prediction. Prescribed actions become jobs in maintenance.
- 01Feeds thisSet up assets
Inventory management
The record everything else depends on.Explore - 02Set up assets
Installation management
One controlled path from site readiness to go-live.Explore - 03Feeds thisMaintain assets
Maintenance management
Fix what broke and service what is due, on one work engine.Explore - 04Feeds thisAsset intelligence
Fault analytics
Understand what is failing now, and why.Explore - 05You are hereAsset intelligence
Predictive and prescriptive maintenance
Know what will fail, weeks before it does, and what to do about it.
Operators
Operators running Iris R-One
Frequently asked questions
Up to 3 weeks. R-One flags the component at risk and when it is expected to fail, early enough to plan the fix, reserve the part and schedule a visit instead of reacting to an outage.
Up to 97% of the chargers R-One predicted would fail went on to fail. Every prediction carries a confidence level, so crews can start with the chargers at highest risk.
Prescriptive maintenance goes one step past prediction. After R-One predicts which component is likely to fail and when, it prescribes what to do about it, and turns that action into a remote fix or a work order.
A single health measure per charger, built from behaviour, fault history and service data, that ranks chargers from Excellent to Failed so crews start with the highest risk.
OCPP telemetry, Logged and Derived faults, service history and asset details such as OEM, model and age.
Yes. Every completed job records what was found on site, and that outcome feeds back into the model.
See R-One on a live network.
Share the size and make-up of the charging network, and the walkthrough will show what R-One surfaces on it. Demos run about 40 minutes.
Offices
