Industrial AI: from failure prediction to energy optimisation
A plant produces data without pause — thousands of signals a second from sensors, telemetry and line cameras. Miras turns that data into models so that an anomaly is seen hours before the line stops, less energy is burned and quality stays consistent.
Four AI applications proven inside the plant
Predictive maintenance (PdM) and failure prediction
The model learns from the equipment’s vibration, temperature, current and pressure data and recognises the pattern that precedes a failure. Maintenance happens at the right time — not too early, and not after a breakdown.
Energy and fuel optimisation
The Miras prediction model recommends furnace, rolling-line or compressor settings to the operator based on live conditions — the method that reached a 92% fuel-efficiency gain at Mobarakeh Steel.
Industrial machine vision and quality control
Line cameras become a tireless inspector: surface defects, dimensions, safety and PPE — in real time and recorded.
Plant knowledge assistant
A Persian language model over your procedures, maintenance logs and standards; the operator asks in their own words and gets a documented answer — on a server inside the plant.
Mobarakeh Steel: when the model listens to the rolling line
Unbalanced fuel consumption on the rolling line.
Miras prediction model on line sensor data; six-month deployment.
92% fuel-efficiency improvement · 24/7 monitoring continues.
The Miras model did not replace the control systems; it sat beside them, read the line sensors, found the pattern of unbalanced consumption and gave the operator setting recommendations. Six months later fuel efficiency was 92% better, and 24/7 monitoring continues.
Steel, copper, oil & gas, mining, telecom
- Steel and copper: fuel and energy optimisation, failure prediction for rotating equipment, visual quality control.
- Oil & gas: monitoring of critical equipment, anomaly detection in process data, technical knowledge assistant.
- Mining: image and machine data analysis, fleet maintenance planning.
- Telecom: failure prediction, network report analysis, response automation.
Field experience
Experience across 14+ major industries. Contact us for a case study from your industry.
How it is deployed in your plant
Data audit
Which sensors, which history, which stoppages. Output: a data map and one target indicator.
Pilot on one line
The model is trained on your real data and predicts beside the existing system, without touching control.
Deployment on your server
On a server inside the plant or on Miras infrastructure in Tehran; by default the data stays in Iran.
24/7 monitoring
The model is refreshed with new data; the target indicator is reported monthly.
Frequently asked questions
What is predictive maintenance and how does it differ from calendar maintenance?
Calendar maintenance repairs on a schedule whether needed or not. Predictive maintenance uses an AI model on the real equipment data (vibration, temperature, current) to forecast the likely time of failure, so repair happens at the right moment — fewer unplanned stops and less premature part replacement.
What data is needed to start?
Usually the sensor history (PLC/SCADA or a historian), stoppage and maintenance records and, where available, line images. If the data is scattered, the audit shows exactly that and the pilot starts where data is sufficient.
Does plant data leave Iran?
Not by default. Miras models run on a server inside the plant or on Miras’s own GPU infrastructure in Tehran. By default no data is sent to a foreign cloud service; a foreign fallback model is used only with the customer’s written consent.
Do the models interfere with existing control systems?
No. In the pilot the model only reads and recommends; the decision stays with the operator. Closed-loop control is enabled only with your approval and under the plant’s own safety protocol.
Bring intelligence to your plant or project
We start with a data audit: where your data is, where cost leaks, and which model pays back first. Tehran · Isfahan · Noshahr · Dubai.