The task
Maintenance according to a fixed calendar wastes downtime on healthy machines and risks damaging components that are already under strain. The gap between planned and actually required maintenance is the biggest hidden cost driver in most manufacturing companies.
Predictive maintenance closes this gap. Not with gut instinct, but with signals that every machine sends out anyway. EdgeBrain learns to read these signals directly at the machine, without going through a data center.
The challenge
Machine signals are diverse: vibration, temperature, current, pressure, sound emission. Each sensor type provides a different data structure and different information about the machine status. Conventional monitoring systems are often designed for a single sensor type and therefore only provide partial images. Only the combination shows whether a temperature increase is caused by increased load, wear or a changed machining program.
Added to this are the structural hurdles in practice. Interventions in PLC landscapes, IT/OT architectures or ongoing processes are difficult to implement in most plants. Predictive maintenance systems, which require precisely this, fail due to the reality of existing plants.
The solution
EdgeBrain is an open, sensor-independent system for predictive maintenance. The platform merges data from a wide range of industrial sensors to create a common status image of the machine. Vibration, temperature, current, pressure and noise emissions are processed inline on the device and interpreted together.
The basis is ML fingerprinting. EdgeBrain learns the normal condition of each machine in the first few operating cycles and recognizes any deviation from this from the first cut. Two complementary fingerprints enable seamless condition monitoring. Fingerprint 1 accompanies the tool life continuously. Its amplitude correlates directly with mechanical wear and turns a binary alarm into a real condition system. Fingerprint 2 shows a stable pattern for most of the tool’s life and only rises sharply shortly before the end of its life. This early warning is available in real time so that tool changes can be scheduled for the next planned stop.
EdgeBrain works completely autonomously. Processing, modeling and decision logic run locally on the device, without network dependency, without cloud, without latency. Sensors are attached using magnets and the device is connected as an inline adapter. Existing installations become data-driven systems in just a few minutes, without downtime and without interfering with existing systems.
Key technical data
| Parameter |
Value |
| Sensors |
Vibration, temperature, current, pressure, acoustic emission, other industrial sensors |
| Data fusion |
Multimodal fusion of multiple sensor channels in real time |
| Detection |
Continuous status tracking, early warning at the end of tool life |
| Learning mode |
Unsupervised baseline recording in the first operating cycles |
| Operating conditions |
-40 to +85 °C, passively cooled, 90 x 75 x 25.5 mm |
| Infrastructure |
Completely self-sufficient, no intervention in PLC, MES, SCADA or cloud |
| Integration |
Optional in MES, SCADA, ERP via encrypted connectivity |
The added value
Predictive maintenance with EdgeBrain reduces unplanned downtimes by up to 85% and makes better use of tool life by up to 20%. Maintenance is decoupled from the calendar and is based on the actual condition of the machine. This reduces spare parts costs, increases system availability and allows maintenance teams to work in a planned rather than reactive manner.
The open sensor architecture allows the system to grow in line with demand. From a single critical component to an entire machine. Without new infrastructure, without risk to existing processes and with full data sovereignty in the plant.
