Predict the
failure
A predictive-maintenance platform built for Decker's fastener plant. Vibration sensors on every cold header and thread roller stream high-resolution data into machine-learning models that learn how each machine sounds when it's healthy — and flag developing wear or misalignment before it becomes failure.
Every machine, every cycle.
A vibration sensor on every machine, placed where a developing fault leaves its signature first. The full waveform, every cycle, every shift — no walking-around spot checks, no missed events between routes.
Compact, magnetic-mount accelerometer with on-board edge compute. Drops onto any steel surface, autoconfigures, and starts streaming within minutes — no rewiring, no shutdowns.
- National 2-1/4-S
- Sakamura HM-12
- Nedschroef HQB
- Reed 2-die
- Vencat HC-FL
- Hartford 7-CM
Not just bearings and shafts. Mechanical, electrical, lubrication, process, and structural faults all leave a vibration fingerprint — and the platform's learned models flag patterns that don't fit any known signature, including failure modes we've never described before.
Sense. Learn. Predict.
Vibration is the first place a developing failure shows up — long before heat, noise, or a broken part. We instrument every machine, learn what healthy operation sounds like, and flag the drift before it becomes downtime.
High-resolution accelerometers capture the full mechanical signature of each asset — every cycle, every rotation, every impact. The data is the ground truth: what the machine is actually doing right now.
Advanced machine-learning models build a per-asset profile of healthy operation from real production data. No manual tuning, no per-machine thresholds — the system learns what normal looks like for this header, this roller, today.
As wear or misalignment develops, the model recognizes the drift and surfaces it as a clear health verdict — with enough lead time to schedule the fix on your terms, instead of reacting to an unplanned stoppage.
Every machine, every chunk of data — distilled into one number and a clear verdict: Healthy Watch Elevated Fault. No spectrograms to read, no thresholds to set. The dashboard tells you which assets are drifting, what the suspected cause is, and how much runway you have before it matters.
before unplanned downtime
Verdict, evidence, history.
Each machine page is built around three questions a maintenance lead actually asks: what's wrong, why do we believe that, and how did we get here? Everything else on the page is in service of those three answers.
A 0–100 health score and a clear verdict — Healthy, Watch, or Fault — paired with a plain-English recommendation and a window. "Take this asset out of service before next shift" instead of "anomaly score 0.71."
Live three-axis waveform, power-spectral density, envelope spectrum, and a ranked frequency match. Switch axes, change time-base, zoom into the resonance band — the math is on tap if you want to check our work.
Health over the last 24 hours, 7 days, 30 days, or 12 months. See the maintenance event that pulled an asset back from a fault three months ago — or the slow decline that hasn't been addressed yet.
Six machines. Three axes. Real DSP.
The waveforms are synthesized; the spectra and envelope analysis are computed from them with real FFT and Welch math, not painted on. Click any asset to walk through verdict, evidence, and history.