Energy monitoring
Break down site energy use by machine or process and flag equipment that uses more than it should.
Back to sensors & industrial ML
SENSORS · ENGINEERING · INDUSTRIAL DATA
Your machines,
understood.
Machines, vehicles and production lines already produce a steady stream of sensor data, and most of it is never looked at. Machine learning can turn those readings into earlier warnings, better settings and faster engineering decisions. Here are some of the ways it can help.
01 / IN PRACTICE
Five ways to put
sensor data to work.
Each example below is a common starting point. Real projects are shaped around your equipment, the data you already record and the decisions your engineers need to make.
01 / PREDICTIVE MAINTENANCE
Service machines before they fail.
Vibration, temperature, current and pressure readings change as parts wear. A model trained on your sensor history and maintenance records can track that drift, estimate how long is left and suggest a service window, so work is planned rather than rushed.
- Pumps, motors, fans & compressors
- Engines, gearboxes & vehicle fleets
- Spares and downtime planned ahead
Fewer surprise breakdowns, fewer unnecessary services.
Predictive maintenanceILLUSTRATIVE DEMO
HEALTH INDEX0.52LIKELY CAUSEBearing wearSERVICE BYWed 15 Oct
SERVICE LIMIT
WORK ORDER DRAFTED FOR REVIEW ±4 days
02 / PROCESS OPTIMISATION
Find the settings that make the best product.
Most processes have more combinations of settings than anyone can try. Learning from past production runs, a model can suggest temperatures, speeds and flows that improve yield or reduce energy, always inside the limits your engineers set.
- Extrusion, moulding & mixing
- Drying, heating & cooling
- Yield, quality & energy targets
Better output from the equipment you already have.
Process optimisationILLUSTRATIVE DEMO
- Barrel temperature
212 °C→ 204 °C- Line speed
14.0 m/min→ 15.2 m/min- Cooling water
18 °C→ 16 °C
PREDICTED YIELD91.2% → 94.6%ENERGY PER TONNE −6%
- Within safe limits
ENGINEER APPROVES BEFORE ANY CHANGE trial run
03 / SOFT SENSORS
Measure what you can only test occasionally.
Some qualities — moisture, viscosity, emissions, composition — are measured in a lab every few hours. A soft sensor estimates them continuously from the readings you already collect, so problems are spotted in minutes rather than at the next sample.
- Product quality between lab tests
- Hard-to-measure emissions & efficiency
- Checking a physical sensor has drifted
A live reading without new hardware.
Soft sensorsILLUSTRATIVE DEMO
LAB SAMPLE EVERY4 hESTIMATE EVERY10 sTYPICAL ERROR±0.3%
Model estimateLab sample
ESTIMATED FROM 9 EXISTING SENSORS no new hardware
04 / SIMULATION SURROGATES
Get simulation answers in milliseconds.
Detailed CFD, FEA or process simulations can take hours per run. A surrogate model learns from a set of those runs and then predicts new designs almost instantly, so thousands of options can be screened before the best few are simulated in full.
- Design studies & optimisation
- Engine, thermal & fluid models
- Fast what-if tools for engineers
Explore the whole design space, not just a few points.
Simulation surrogatesILLUSTRATIVE DEMO
DESIGN INPUTS
Fin pitch2.4 mm
Coolant flow1.8 L/s
Inlet temperature85 °C
HEAT TRANSFER14.2 kW
PRESSURE DROP3.1 kPa
FULL SIMULATION6 h 20 mSURROGATE MODEL40 ms
50,000 DESIGN VARIANTS SCREENED ±2% of CFD
05 / ROOT-CAUSE ANALYSIS
Find out why good batches go bad.
When quality varies, the cause is often a combination of factors spread across different systems. Bringing production, lab and environmental data together, a model can rank what explains the variation and point your engineers to the most promising things to test.
- Scrap, rework & warranty returns
- Batch & shift comparisons
- Supplier & material effects
Evidence for where to look first.
Root-cause analysisILLUSTRATIVE DEMO
- FACTORSHARE OF VARIATION
- Resin lot supplier41%
- Ambient humidity27%
- Mould temperature18%
- Shift / operator5%
FINDING TO TEST
Scrap roughly doubles when humidity is above 68% and resin comes from lot B.
FINDINGS FOR YOUR ENGINEERS TO CONFIRM 18 months of data
02 / AND MORE
Other ways
engineering data can help.
Fleet & telematics
Use vehicle data to spot faults early, compare driving patterns and plan servicing across a fleet.
Test data analysis
Summarise rig, dyno and lab test results, compare runs and flag tests that behave unexpectedly.
Signal processing
Clean, align and extract features from high-frequency vibration, acoustic or electrical signals.
Digital twins
Combine physics and data-driven models into a live view of how a machine or process is behaving.
Running at the edge
Deploy models on small devices beside the equipment, where connectivity is limited or data must stay on site.
03 / GETTING STARTED
What a good project
needs to succeed.
Sensor history with timestamps
Readings stored at a useful rate, ideally covering months of normal running and some of the events you care about.
Records of what happened
Maintenance logs, lab results or quality records that tell the model what the readings led to.
Engineers in the loop
Time with the people who know the process, so the model respects the physics and its suggestions make sense.
A safe way to act on it
Suggestions are reviewed before anything changes on the plant, and models stay within agreed operating limits.
04 / YOUR DATA, PUT TO WORK
Sitting on
sensor data?
Tell me about your equipment and what you already measure. We can talk through whether machine learning is a good fit and what a small, well-tested first version could look like.
Open to projects in North Yorkshire and nationwide