Industry: Every machine, watched as it runs.
Systems that turn sensor, camera and production data into decisions on the factory floor.
What we build
Machine monitoring
Vibration, temperature, current and pressure from PLCs, historians and gateways over OPC UA or MQTT, checked against each machine’s normal behavior.
Visual quality inspection
Cameras on the line and vision models that flag defects as parts pass. Images and decisions are stored, so a person can review any call.
Predictive maintenance
Early signs of failure, found in sensor history and maintenance records and raised as work orders with the evidence attached.
Production forecasting
Output and yield forecasts per line and shift, from production history, orders and supply data, updated as conditions change.
Assistants for the floor
Answers for operators and technicians from manuals, procedures and maintenance history, each with its source.
Edge, plant or cloud
Models run where the data is: on an edge server by the line, in the plant’s data center or in your cloud, managed from one place.
Defects caught at line speed.
How it works
Sensors and cameras feed an edge server beside the line, where the inspection and maintenance models run. Events flow on to the plant historian, and findings land as work orders or in the operations queue.
Repairs planned, not forced.
Why it holds up
Processed at the edge
High-rate signals are reduced next to the machine, and only features and events travel upstream. Raw samples stay local for investigation.
Works offline
Edge nodes keep inspecting and scoring when the plant network or the cloud link drops, and sync when it returns.
Inside the cycle time
Inspection fits within the line’s cycle time, with a defined action (hold, reject or pass) when a model is unsure.
Rolled out line by line
Models are versioned, deployed one line at a time and rolled back in one step, with drift watched site by site.
What we measure
- Defect escape rate
- False reject rate
- Unplanned downtime
- Mean time to repair
- Overall equipment effectiveness
- Forecast error
Common questions
What does Stelcorp build for industrial companies?
Stelcorp builds systems that turn sensor, camera and production data into decisions on the factory floor, for manufacturers and industrial operators: machine monitoring, visual quality inspection, predictive maintenance, production forecasting, and assistants that answer operators and technicians from manuals, procedures and maintenance history.
Which machine data do the systems use?
Vibration, temperature, current and pressure from PLCs, historians and gateways over OPC UA or MQTT, checked against each machine’s normal behavior. Cameras on the line feed visual inspection, and forecasts draw on production history, orders and supply data.
Do the models run on-premises, at the edge or in the cloud?
Where the data is: on an edge server by the line, in the plant’s data center or in your cloud, managed from one place. Edge nodes keep inspecting and scoring when the plant network or the cloud link drops, and sync when it returns.
Can visual inspection keep up with the line?
Inspection fits within the line’s cycle time, with a defined action (hold, reject or pass) when a model is unsure. Images and decisions are stored, so a person can review any call.
How does an industrial project start?
With one production line or one class of machines. Stelcorp starts from its data and a baseline, and ends the three-week assessment with a working prototype. Models are then rolled out line by line: versioned, rolled back in one step, with drift watched site by site.
Start with one line.
One production line or one class of machines. We start from its data and a baseline, and end the three weeks with a working prototype.
Tell us about the line