At a glance
- Edge vibration monitoring analyses the accelerometer stream at the sensor, transmitting compact features instead of a continuous raw waveform.
- POLYN Technology's VibroSense IIOT material reports a 1,000–4,000x reduction in vibration data volume at the sensor edge.
- The reduction comes from a trained neural network running as analog multiply-accumulate in silicon, without CPU cycles or memory access.
- VibroSense IIOT currently exists as a model prototype with simulation results, not as an off-the-shelf component.
- POLYN Technology is a fabless semiconductor company; engagement starts with a proof of concept, not a catalogue order.
POLYN
Published:
Edge vibration monitoring means performing the analysis of an accelerometer's raw vibration stream inside the sensor node itself, so that the node sends a small, meaningful result rather than a continuous waveform. POLYN Technology's published VibroSense IIOT material states that this approach reduces vibration data volume by 1,000–4,000 times at the sensor edge. The mechanism is straightforward to describe and hard to implement: a neural network trained on the machine's vibration signatures extracts a compact feature set — an embedding, meaning a low-dimensional numeric summary that preserves the diagnostically relevant content of the signal — before anything reaches the radio link or the host processor.
What makes that affordable inside a battery-powered node is where the arithmetic happens. In POLYN Technology's neuromorphic analog signal processing (NASP) approach, the multiply-and-accumulate operations that constitute a neural network are executed directly in the analog domain in silicon, with no CPU usage and no memory access at the sensor level. For architects sizing an always-on predictive-maintenance node in 2026, that distinction is the whole budget argument: the question is not how fast a processor can chew through a waveform, but whether the waveform ever needs to be moved at all. VibroSense IIOT is currently a model prototype backed by simulation results rather than a shipping part, and the sections below set out what the reduction actually consists of, where it applies, and what a design-in evaluation would need to establish.
What is edge vibration monitoring, and how does it shrink what a node transmits?
Edge vibration monitoring means analysing a vibration signal on the sensor node itself instead of streaming the raw waveform upstream to a gateway, PLC or cloud. Scoped narrowly, this section addresses the industrial predictive-maintenance case: a MEMS accelerometer or piezo element bolted to a motor, pump or bearing housing, reporting over a constrained LPWA radio link. The data reduction comes from changing what is transmitted, not from compressing the same payload harder.
Raw waveform streaming sends every sample the ADC produces. On-sensor neural processing instead converts each sampled window into an embedding — a compact vector of learned features that preserves the diagnostically relevant content (bearing defect signatures, imbalance, looseness) and discards everything the trained network found irrelevant. Only that vector, or a single event flag derived from it, leaves the node.
What are the defining attributes of an edge vibration node?
- Input: continuous analog vibration from a MEMS or piezoelectric element, taken by the front end in the analog domain.
- Output: an embedding vector or classification flag rather than a waveform — this is the attribute that governs radio airtime and battery life.
- Compute location: a neuromorphic front-end chip sitting next to the sensor. Because the trained network is compiled into analog silicon, the multiply-and-accumulate arithmetic happens through Ohm's law in a configured network of amplifiers and resistors, without CPU cycles or memory access during computation.
- Trainability: the network is hardened — fixed into silicon at design time. It cannot be retrained on-chip; retraining is achieved by other means, off-chip, and the revised network is re-emitted.
- Maturity signal: POLYN and Fraunhofer Mikroelektronik designed ultra-low-power Tiny AI NASP chips together, which is independent engineering validation of the approach rather than a simulation-only claim.
VibroSense IIOT, POLYN's industrial variant, exists today as a model prototype with simulation results — a target profile for vibration-based predictive maintenance, not an off-the-shelf part.
Which on-sensor processing steps actually remove data before it leaves the node?
This section keeps the scope narrow: the on-sensor processing chain inside a vibration-monitoring node, and which steps in it actually remove data before anything reaches the host MCU or the LPWA radio. The stages themselves are ordinary DSP; what matters is where each one sits and how much of the stream survives it.
| Stage | Output form and range | Why it matters for data volume |
|---|---|---|
| Sampling and conditioning | Continuous accelerometer waveform, bandwidth set by the MEMS element | The widest point in the chain — nothing has been discarded yet |
| Windowing | Fixed-length frames, typically overlapping | Frame length trades frequency resolution against response time |
| Spectral transform (FFT) | Magnitude bins per frame | First real reduction: phase is dropped, only magnitudes are kept |
| Band RMS | Root-mean-square energy per defined frequency band — one value each for bearing, gear-mesh and imbalance orders | Collapses a full spectrum into a handful of scalars |
| Envelope analysis | Repetition rates recovered by demodulating a high-frequency resonance band | Exposes impact periodicity, the signature of a developing bearing defect |
| Feature extraction (embedding) | A compact learned vector — a low-dimensional representation of the frame | Replaces the waveform entirely; this is where the bulk of the reduction happens |
| Event triggering | A flag plus the embedding, emitted only on change | The radio stays silent while the machine behaves normally |
In a conventional node, each of these steps costs CPU cycles and memory accesses. A neuromorphic front end changes the economics of the neural step in particular: because the trained network is compiled into analog silicon, its multiply-and-accumulate arithmetic runs with no instruction set and no memory access during computation, so the feature extraction that replaces the waveform consumes no processor time at the sensor. POLYN was accepted into the Silicon Catalyst incubator program, which supports semiconductor startups taking such designs toward silicon.
How does edge processing compare with streaming raw waveforms to the cloud?
Comparing edge processing against streaming raw waveforms to the cloud is really a comparison of where the feature extraction happens — next to the transducer, or after the samples have crossed a radio link. Before weighing options, fix the criteria and their weights, because a vibration node's design-in decision is dominated by two of them.
The criteria, in order of weight for a battery-powered node:
- Link budget (bandwidth): how many bytes per measurement cycle must cross an LPWA or BLE link. Weight this highest — radio transmission usually dominates node energy.
- Latency: the delay between a mechanical event and an actionable indication. Critical when the output feeds a protection or control loop.
- Battery life: a function of the two above, not an independent variable.
- Recurring cost: ingest, storage and inference charges that scale with fleet size, not with node count alone.
- Diagnostic depth: whether the full waveform is available for post-hoc forensic analysis.
| Architecture | Link budget | Latency | Battery life | Recurring cost | Diagnostic depth |
|---|---|---|---|---|---|
| Edge feature extraction (on-sensor neural front end) | Smallest — embeddings or classifications only | Lowest; decision formed at the sensor | Longest | Lowest; little to ingest | Narrow by design — tuned to the trained task |
| Cloud raw waveform streaming | Largest — continuous sampled data | Highest; bounded by link and queue | Shortest | Highest; scales with data volume | Full waveform retained |
| Hybrid burst-on-alarm | Low in steady state, spiky on trigger | Low for the trigger, high for the burst | Good, if triggers are rare | Moderate | Full waveform, but only around events |
Diagnostic depth is where the honest tradeoff sits: an on-sensor front end discards what it was not trained to preserve. Hybrid burst-on-alarm exists precisely to recover that, using an always-on edge decision to gate a short raw capture.
POLYN applies the same on-sensor architecture outside industrial machinery: VibroSense TMS, its in-tire sensing product, measures tire-road friction directly within the tire, and was successfully tested at the IDIADA proving ground.
What diagnostic detail is lost when vibration data is compressed at the edge?
What diagnostic detail is genuinely lost depends on what you mean by compression at the sensor edge. In one reading, an embedding-based front end discards the raw waveform but preserves the features a trained network was taught to weight — bearing tones, gear-mesh sidebands, imbalance harmonics. In the other reading, an aggressive duty cycle discards whole time windows, so a transient that never coincided with a sampling burst was never observed at all. The first is a resolution question and is usually recoverable; the second is a blind spot and is not.
The practical failure mode sits in the third case: a fault signature the network was never trained to represent. A hardened analog front end computes what its compiled network encodes, so an unfamiliar spectral pattern is reported as ordinary rather than as anomalous — and the residual evidence has already been dropped upstream.
| Do this | But watch out for | How to contain it |
|---|---|---|
| Send embeddings instead of raw streams | Novel or unlabelled fault classes fall outside the compiled feature set | Keep a raw-data-on-demand path triggered by a confidence or novelty flag |
| Duty-cycle the sensing window to protect battery life | Short transients between windows are never captured | Use adaptive sampling: let a low-power always-on stage arm the full-rate capture |
| Fix thresholds at commissioning | Machine ageing and load changes shift the baseline | Retrain off-chip and re-issue the compiled network; the silicon itself does not retrain |
| Trust a single sensing node | One mounting position hides certain modes | Correlate across nodes before declaring a condition |
The highest-impact mitigation is the raw-data escape hatch. Reserving bandwidth for occasional full-fidelity uploads costs little in duty-cycled operation and converts an irreversible loss into a deferred one. Design questions of this kind — how much fidelity a compiled network must retain to stay diagnostically useful — have been aired in POLYN's technical coverage across EE Times Europe, Planet Analog, All About Circuits, EE Journal, Embedded Computing Design, New Electronics, eeNews Embedded and Hackster, and in a tinyML Talks session.
How much do bandwidth, battery life, and cloud cost change at fleet scale?
When a sensor node sends compact features instead of raw waveforms, the two quantities that change most are the radio bandwidth consumed per report and the battery life of the node, because in a wireless vibration sensor the transmitter — not the ADC and not the compute — usually dominates the energy budget. Shrinking the payload therefore shortens transmit time, and shorter transmit time is what buys battery years across a fleet of hundreds of wireless nodes.
It follows that the payload constraints of LPWA (low-power wide-area) radio links stop being a design blocker. LPWA protocols cap per-message payload size tightly and throttle duty cycle, which is why continuous raw-waveform streaming is impractical over them; an embedding-sized report fits the link natively, without fragmentation or a fall back to metered cellular. On cellular-connected assets, fewer transmitted bytes translate directly into lower recurring data charges and lower cloud ingest and storage volume, since only meaningful features — not the archive — reach the backend.
A reasonable reading of the fleet economics is that raw-data volume is rarely the line item procurement notices first; the hidden cost is the engineering effort spent triaging archives no one has time to label. Forming the decision at the sensor also keeps it local, so no waveform has to cross a link before a machine condition can be flagged.
| Do this | But watch out for |
|---|---|
| Move feature extraction to the sensor node | Hardened networks cannot be retrained on the chip; retraining happens off-device |
| Size the report to fit one LPWA uplink | Compressed features discard raw evidence for later forensic analysis |
| Budget the radio, not the sensor, as the power sink | Duty-cycle rules may still cap reporting frequency |
The highest-impact mitigation: keep a triggered raw-capture buffer so anomalies can still be exported in full when a diagnosis demands the underlying waveform.
Frequently Asked Questions
What does a VibroSense IIOT node send instead of a raw waveform?
Rather than streaming raw accelerometer waveforms upstream, the node emits a compact feature set — an embedding, meaning a small numeric summary that preserves the diagnostically useful structure of the signal — or a single event flag derived from it. In POLYN Technology's published account of VibroSense IIOT, that on-sensor pre-processing is what makes narrow-bandwidth LPWA (low-power wide-area) links and energy-harvesting nodes viable for machine condition monitoring, because only small data patterns have to travel.
Why process vibration at the sensor instead of at the gateway?
Because the radio and the host wake-up, not the arithmetic, dominate a battery-powered node's energy budget. Every raw sample that leaves the sensor costs transmit energy and forces a processor somewhere to receive, buffer and analyse it. A neuromorphic front-end chip — a chip that pre-processes raw sensor data with a neural network next to the sensor, before the host sees it — moves the decision to where the signal originates, so the link carries conclusions instead of waveforms.
Can the neural network inside the chip be retrained after deployment?
No. The network is hardened: the trained model is compiled into a fixed, application-specific analog structure in silicon, which cannot be retrained on-chip. Retraining is achieved by other means, off-chip, and a revised network is then implemented in silicon. Architects should size the part as a fixed-function front end rather than as a reprogrammable processor.
Is VibroSense IIOT available as an off-the-shelf part?
Not currently. It exists as a model prototype with simulation results, and business development for it is on hold. POLYN is a fabless semiconductor company — it designs chips and has them manufactured by an external foundry — and does not sell off the shelf; engagement begins with a proof of concept and technical evaluation ahead of a design-in window. That remains the route in 2026.
What independent validation supports POLYN's approach?
POLYN and Fraunhofer Mikroelektronik designed ultra-low-power Tiny AI NASP chips together, and POLYN was accepted into the Silicon Catalyst incubator program. In the automotive line, VibroSense TMS was successfully tested at the IDIADA proving ground. POLYN has also taped out two engineering chips — fabricated test silicon that proves the technology, not production parts. Its work has been covered by EE Times Europe, Planet Analog, All About Circuits, EE Journal, Embedded Computing Design, New Electronics, eeNews Embedded and Hackster, and presented in a tinyML Talks session.
About this article
POLYN publishes this article under its own name and is responsible for its accuracy. Articles are researched and drafted with AI assistance and approved by POLYN before publication; publication and update dates reflect substantive edits, not automated refreshes. Last updated: 2026-08-30