AI platform
Early warning intelligence.
Practical deployment control.
Detect drift before failure, keep complete historical captures, and deploy where your operations and compliance requirements demand.
AI early warning
AI that warns you days before failure
The edge device is your real-time protection layer — it reacts in milliseconds. The AI is your early warning layer — it thinks in days and weeks.
"Dark-themed industrial IoT monitoring dashboard UI screenshot. Left sidebar shows a list of machines with health scores (green/amber/red). Main panel shows time-series vibration charts for 3 sensors over 30 days with an anomaly detection alert highlighted in amber at the 25-day mark. Clean data visualization with blue and purple gradient accents on dark navy background, professional SaaS interface design, 16:9 aspect ratio."
dashboard.png — replace with real screenshotHow the model is trained
Mark a healthy period
Select a date range when the machine was running normally — no known faults, stable load. This becomes the reference window the model will learn from.
System trains a baseline
VibraFabrics trains on the scalar metrics computed during that healthy period: vibration severity, frequency bands, temperature ratios, and any other linked signals. The result is a learned "normal" signature for that specific machine at that operating point.
Alerts when behaviour drifts
Once trained, the anomaly detector runs continuously. When the live signal deviates from the learned baseline beyond the configured sensitivity, an early warning is raised — days or weeks before a threshold alarm would trigger.
"Dark industrial software UI screenshot showing a 'healthy period' training configuration panel. A horizontal timeline chart spans the top with a green highlighted date range selected (approximately 3 weeks). Below it, a vibration trend line stays flat and stable within the selection. A sidebar on the right shows 'Training status: Ready', sensitivity slider, and a blue 'Train model' button. Dark navy theme with subtle grid lines, professional B2B SaaS aesthetic."
training-config.pngSensor linkages
Train across sensors, not just on one at a time.
Real machines don't fail in a single sensor in isolation. A bearing degrading in a pump affects vibration, temperature, and process flow simultaneously. VibraFabrics lets you define sensor groups — linking the signals that belong to the same machine or drivetrain.
The anomaly detector trains on the group as a whole, learning the normal correlations between linked sensors. A deviation is flagged when the pattern across the group shifts — not just when a single sensor crosses an arbitrary threshold. This dramatically reduces false alarms while catching subtle multi-signal anomalies that single-sensor thresholds miss entirely.
- ✓Group any combination of sensors — vibration, temperature, current, process signals
- ✓Configure sensitivity per group — critical machines can be set more aggressive
- ✓Retrain at any time — after a repair, an operating point change, or a seasonal shift
"Dark UI panel showing a sensor grouping / linkage configuration interface. Several sensor nodes displayed as rounded-rectangle cards (labels: 'Motor NDE bearing', 'Motor DE bearing', 'Motor winding temp', 'Pump discharge pressure'), connected by glowing lines indicating they belong to the same 'Motor-Pump group'. A trained anomaly score gauge shows 'Normal' in green. Dark navy interface with violet and blue accents, professional industrial monitoring SaaS aesthetic, portrait orientation."
sensor-linkages.pngConfigurable anomaly detector
Set sensitivity per machine group — aggressive on critical assets, conservative on less important ones. Tune to your actual false-alarm tolerance.
Root cause indication
When an anomaly fires, the system highlights which sensors inside the group are contributing most — helping your team target their inspection immediately.
Future-proof data store
Raw signals are stored at full resolution — not just the scalar metrics. As AI techniques advance, historical captures can be re-analysed with more sophisticated algorithms.
Unified multi-site view
All wired and wireless sensors, all sites, all anomaly alerts in one dashboard. Role-based access and tenant isolation built in.
Raw data archive
Keep full-resolution captures so future models can re-analyze the past.
Each acquisition stores full raw signals, not only aggregated metrics. That means new analytics and AI techniques can be applied to historical datasets without replacing edge hardware or repeating data collection campaigns.
Deployment and sovereignty
Run in Azure or fully on-premises without changing platform capabilities.
VibraFabrics supports cloud and customer-hosted deployment models with the same dashboard, anomaly detection, and archive behavior. Choose the option that fits your governance, latency, and security posture.
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