Stop Shutting Down Healthy Equipment
Predictive models are only as good as the sensor data they consume. Expanso validates PLC and SCADA telemetry at the source, so your maintenance decisions are based on signals you can trust.
No infrastructure replacement. No pipeline rewrites. Works alongside your SCADA and historian.
Predictive models inherit every flaw in the data they consume
Sensor drift, duplicate readings, and ingestion lag don't just add noise - they cause models to recommend shutdowns on equipment that's running fine, and miss failures that are actually developing.
5-15% of sensor readings are duplicates from retransmissions
When a PLC retransmits data after a network hiccup, those duplicate readings enter your historian and get consumed by predictive models as if they were independent observations. Vibration trends spike. Pressure baselines shift. Your maintenance team investigates equipment that never had a problem.
Ingestion lag shifts model baselines by seconds or minutes
When batch data arrives late, your models compute features on windows that don't align with physical reality. A 3-second delay on a 500ms vibration signal makes the model's frequency analysis meaningless.
Dropped PLC samples create false vibration trends
When a PLC drops 2% of readings, the resulting gaps in your historian look like sudden transitions. Your predictive model interprets them as equipment degradation patterns.
Incomplete data windows inflate false positive rates
When a predictive model operates on a window that's missing 5% of its data points, the confidence interval widens and every marginal reading gets flagged. More false positives mean more unnecessary shutdowns and inspections.
Sensor integrity, enforced before ingestion
Expanso validates every PLC reading, SCADA signal, and sensor stream at the source. Your historian and predictive models only receive data that passes validation.
What we validate
Sample completeness - every PLC reading in the window is accounted for
Duplicate suppression - retransmitted readings stripped before ingestion
Timestamp accuracy - sequence and timing validated against physical constraints
Sensor drift detection - readings that diverge from calibration baselines are flagged
What your systems receive
Complete data windows with no gaps or dropped samples
Deduplicated streams that reflect actual equipment behavior
Time-consistent records that align with physical process windows
Drift alerts that arrive before corrupted data reaches your models
Large-Scale Deployment
14,847 distributed endpoints. 4.7 PB/month. $4.3M quoted for analytics.
A major U.S. deployment across 14,847 endpoints validated Expanso's approach to edge-first data integrity. Processing moved to points of presence, raw data stayed local, and only metadata and flagged events flowed upstream.
"We already have SCADA and predictive analytics."
You do. SCADA visualizes process data. Your historian stores it. Your predictive models consume it. None of them verify that the sensor telemetry reaching them is complete, time-consistent, or free of duplicates.
Expanso validates PLC and sensor data at the source before ingestion. Your SCADA, historian, and models stay exactly where they are - the difference is they now operate on signals you can trust.
Built for energy infrastructure
Works across SCADA, PLCs, and sensor networks from any vendor
No disruption to control systems or operational workflows
Deploys incrementally, site by site, without downtime
Scales from a single compressor station to an entire fleet
Why teams deploy Expanso
Runs at the edge, where the equipment operates
Doesn't replace your analytics - makes them accurate
Reduces false maintenance alerts and unnecessary shutdowns
Improves predictive model accuracy by fixing input quality
If the sensor data isn't clean, the maintenance decision isn't safe
Fix the signal at the source. Let your models do their job.