Inside the dashboard
Six tabs, one motor, zero guesswork. Click through what the team actually sees.
Live status at a glance — health score, return torque, and days-to-alert for every operating mode, updated the moment new data lands.
Industrial IoT · Predictive Maintenance Client project
Blackbox Factories runs a continuous shuffler line on a Mitsubishi servo motor. A breakdown means line downtime and missed delivery windows. They had sensor data — but no way to read it until it was too late.
The shuffler line's servo motor was instrumented with PLC sensors streaming data at 250Hz — but that data just sat there. Nobody on the floor had a way to translate raw torque and timing signals into a simple answer: is this motor healthy, or is it heading toward a breakdown?
Without that answer, the only options were reactive — wait for a failure, then scramble — or overly cautious preventive maintenance on a fixed schedule, which wastes time servicing a motor that's perfectly fine.
We needed a system that didn't just flag a problem after it occurred, but projected forward — telling the team how many days they had before action was needed.
Six tabs, one motor, zero guesswork. Click through what the team actually sees.
Live status at a glance — health score, return torque, and days-to-alert for every operating mode, updated the moment new data lands.
The model caught a forward-stroke friction pattern building over several days — one that would have caused a stoppage if left unaddressed. Instead of an emergency repair, the team had a clear 5-day window to schedule maintenance on their own terms.
The dashboard's health score dropped from a healthy 99.6 to a cautionary 46.9 well before any visible symptom on the line — turning a sensor stream nobody was reading into a daily 30-second check anyone on the floor could do.
"Most companies are sitting on years of data that could answer their most important questions. They just need someone to ask it correctly."