Predictive Maintenance Alerts

Catch hardware degradation before it causes an outage, hazards, or costly disruptions.

Not every failure is sudden. Some start subtly, with subtle timing anomalies, power inconsistencies, or signal drift that suggests wear, damage, or environmental stress.

Sensor Biometrics captures these changes at the physical layer, comparing each signal against a trusted baseline. When a device behaves abnormally, we alert your team as soon as the deviation is detected, so they can take action before it impacts operations.

Spot the Problem Before It Breaks

As hardware ages or degrades, radio frequency (RF) emissions shift. Those shifts often appear long before a complete failure or operational symptoms appear. Sensor Biometrics continuously monitors for these small but meaningful changes so you can address issues before they become outages, compliance failures, or safety risks.

Detect signal drift from vibration, corrosion, or board-level fatigue

Identify abnormal activity in COTS and embedded devices

Receive alerts when performance dips below baseline trust

Stay ahead of failure curves without invasive diagnostics

Passive Monitoring. Active Value.

Our system monitors devices without installing agents, interrupting operations, or accessing firmware. It’s passive and continuous, ideal for environments where uptime is crucial.

Real-time signal classification

Effective in legacy and multi-vendor environments

No software integration or device access required

Works in high-noise, real-world conditions

If it emits a signal, we can tell when it’s slipping.

Extend Device Life. Reduce Downtime.

Sensor Biometrics gives you early insight into the health of your connected hardware so you can maintain it before failure, not after it.

Protect uptime. Improve planning. Operate with greater predictability and control.

Know Before It Breaks

Don’t wait for a shutdown or field failure to learn that something was wrong. Every device tells a story. Sensor Biometrics helps your team act early, based on real signal data.