Embedded condition monitoring
Sensors and models integrated into the machine design, with microphone and accelerometer placement chosen for the enclosure.
Industry
Machine and tool builders
Embedded sensing and AI as a feature of your machine, engineered with your R&D team.
The situation
What plants in this sector tell us
- A monitoring feature promised in the brochure and still unbuilt two model years later.
- Sensors that work on the prototype and fail once the enclosure, the coolant and the customer's hall are real.
- No way to learn from the installed base without asking customers for their production data.
- A unit-cost target that rules out every architecture the AI team proposes.
Machine builders want monitoring built into the product, not bolted on afterwards. We co-develop the sensor integration, the models and the edge deployment with your R&D engineers, sized for your controller and your unit economics, and hand over a dataset and a fleet-ready MLOps setup so the feature keeps working across thousands of machines in the field.
Where we help
Where our methods apply
Process quality features
Weld, tool-wear and quality prediction as product functions, and an acoustic end-of-line test for the machine itself before it ships.
Fleet learning
Federated learning across installed machines without collecting customer data.
Funded R&D projects
Support for publicly funded and university-linked development.
EvidenceProject experience with German machine-building SMEs, including microphone placement for acoustic monitoring built into the machine. For German machine builders the development is usually structured as a ZIM co-operation project, which recovers a share of the eligible cost.
By asset
The machines we listen to in this sector
Bearings and gearboxes
Wear, lubrication loss and race defects from vibration and airborne acoustics; on-node classification for fleets.
Welding cells
Laser and arc welds monitored in-process by airborne acoustic emission; the same pattern is proposed for resistance and ultrasonic welding.
Machine tools
Tool condition and process quality as product functions, fused with spindle load.
How we would start
The services this sector uses most
Almost every engagement begins with a feasibility study or a discovery call, because the cheapest thing to establish first is whether the signature is there at all.
Co-development R&D and PoCApplied R&D on your process, with your engineers.Open service page Energy-efficient edge deploymentModels sized for the hardware and the power budget.Open service page Training & capability buildingWorkshops and mentoring for your own engineers.Open service page Multi-modal sensing + edge AIRuggedised sensor units and on-device AI.Open service page
Start here
Discuss a project in Machine & tool builders
Tell us the asset or the station, what goes wrong and how often. We answer every enquiry within 48 hours on working days.
- Your data stays on site; on-premise and on-device deployment is the default
- We work alongside the monitoring system you already run
- Honest go / no-go before any large spend
Ready to find out if it works on your machines?
Start with a free 30 to 45 minute discovery call. Bring the problem, the recordings you already have and your questions. We will tell you honestly whether sensing-based AI is the right tool and what the next step would cost in days, not months.