Automotive & Mobility
Process flow and quality consistency,
controlled with data
Equipment and energy data scattered across press, body, paint and assembly lines is connected through an ontology, and an AI agent catches early signs first and explains the cause. From rotating-equipment predictive maintenance to per-line power analysis and output-linked energy intensity.
These are the problems on
automotive and mobility sites today
- One process shakes the whole line
- Press, body, paint and assembly are coupled end to end
- A single machine stops the whole line
- High power and fine control at once
- High-power equipment shares the line
- Fine control runs on that same line
- Tuning one side makes the other unstable
- Equipment, energy and output stay apart
- Electrification demands all three together
- Quality standards keep tightening
- The data sits in separate systems
- Nothing connects across those systems
This is how
Refinery solves it
Vibration-based predictive maintenance for motors and rotating equipment
- Problem
- Rotating-equipment anomalies confirmed only after a stoppage
- Approach
- An AI agent reads anomalies in vibration and current trends to catch early signs, and proposes both the likely cause and the next action with the history behind it.
- Outcome
- Less equipment downtime and steadier production
Power usage patterns by line
- Problem
- Different power draw line to line, with no way to explain it
- Approach
- Lines, processes and assets are connected through an ontology, so each signal is given meaning — which asset it belongs to and what it measures — and power use can be compared on equal terms.
- Outcome
- Lower energy cost and inefficient processes identified
Demand-based compressed air control
- Problem
- Air compressors running regardless of actual demand
- Approach
- Power draw and operating patterns are read so a VSD matches supply to demand, with inspection cycles and fault diagnosis managed alongside.
- Outcome
- Minimal energy waste and better operating efficiency
Energy intensity tied to output
- Problem
- No structure for seeing efficiency against production
- Approach
- Production records and energy use are linked to derive energy intensity by line and vehicle model automatically.
- Outcome
- Efficiency made visible against output, cost structure improved
Ambient conditions and quality impact
- Problem
- Causes of quality issues recorded in separate systems and hard to retrace
- Approach
- Temperature, humidity, equipment state and quality results are joined on one timeline to trace the cause.
- Outcome
- Less quality variance and better process conditions
How it fits together
How field data gains meaning and turns into a decision.
- Presses
- paint
- assembly equipment
- motors
- vibration sensors
- SCADA
- PLC
- MES
- Modbus TCP/IP
- RS-485
- OPC UA
- Ontology
- AI agent
- rules and automation
- Line dashboard
- predictive maintenance alerts
- quality traceability
Where MES or PLC already exists, a layer sits on top of itand integrates bidirectionally rather than replacing it.Where none exists, collection is built from the ground up.
Systems we connect to
The systems and protocolscommonly used on automotive sites.
Systems not listed here can still be connectedover standard protocols and APIs.Get in touch and we will walk through it.
What you gain
Benefits across operations, engineering and management.
Less equipment downtime
Anomalies are caught early in rotating-equipment vibration, keeping production steady.
Lower energy cost
Power patterns are compared line by line to identify inefficient processes.
Better cost structure
Efficiency against output is put into numbers.
Less quality variance
Ambient conditions and quality results are joined to tune process settings.
