Edge AI for machine health
We provide intelligent on-edge failure detection that is engineered to your latency target and runs inside the control loop.
What we do
Vendor-independent, built for a defined set of failure modes, and validated on simulated and real fault data. You bring the domain knowledge of your machine; we assist in sensing, and provide detector design and hardware implementation.
Where to place sensors in a machine so a given set of failure modes actually becomes observable.
Observability analysis and experimental design on simulation models of your machine, so instrumentation is decided on evidence rather than habit.
A combination of adaptive and AI-based detectors, complementary in what they catch and how fast, engineered against an explicit target detection latency.
Physics- and simulation-informed design with stated uncertainty bounds and worst-case scenarios before anything reaches your machine.
Intelligent signal analysis runs on-edge on microcontrollers and FPGAs, inside the control loop, fast enough to act on a fault while it is still forming.
Delivered as firmware or as an IP core for an FPGA already in your bill of materials. No cloud connection required.
Why Envonetic
Don't let compute dictate your machines' reaction time. We provide novel AI hardware accelerators that target time-critical applications, processes, and failure modes where microseconds decide whether a part is scrapped, a component is damaged, or a process escapes stability.
Where it pays off
Examples from precision machining, the first domain we work in. Each one is a fault that forms faster than periodic analysis can react.
Problem
A chipped edge drifts parts out of tolerance; a breakage can wreck the workpiece and the spindle in one revolution.
How we catch it
Spindle-current and vibration detectors, placed for observability of the cutting zone, separate normal wear from chipping, and stops the feed before a broken tool damages the workpiece or spindle.
Problem
Self-excited tool-workpiece vibration scars the surface, shortens tool life, hammers the spindle and can escalate from stable to unstable during one pass of the tool.
How we catch it
A fast detector tracks the onset signature of regenerative instability as it grows, giving the control system a usable window to act before the surface is marked.
Problem
Too much energy density and the vapour depression under the beam collapses into an unstable keyhole, trapping gas as spherical pores. Those pores become crack initiation sites, so the part's fatigue life drops sharply while it still looks sound.
How we catch it
A detector running on the process signals in the machine flags the transition from stable conduction mode to an unstable keyhole as it happens, while laser power is still adjustable. Controlling the beam within microseconds helps to prevent such structural defects.
The same approach applies to
Let's talk
Come with one machine, one failure mode and the time you would need to react. We will sketch a detector for it: which signals, where to sense them, and what hardware it takes.
Who we are
Co-Founder
Doctoral researcher at TU Munich, Chair of Integrated Systems, on adaptive AI for real-time analysis of high-frequency sensor data. He is lead author of ReLiCADA, a published reservoir-computing design algorithm, and works in FPGA design and embedded neural networks.
LinkedIn profile of Jonas Kantic (opens in a new tab)
Co-Founder
Aerospace and robotics engineer; industrial PhD at Airbus Defence and Space on onboard anomaly detection for spacecraft telemetry. He built machine-learning fault detectors for multivariate sensor streams and ran them on space-grade FPGA and real-time hardware.
LinkedIn profile of Andreas Koch (opens in a new tab)