Midstreamly reads your existing AVEVA PI, Bently Nevada, and Emerson Ovation data to score compressor and pump degradation in real time—delivering fault-mode alerts inside the SCADA tools your operators already use.
Track every unit's health in real time against its own 18-month operating baseline
Each pump and compressor gets its own model trained on 18 months of AVEVA PI historian data. Every 15 minutes, Midstreamly computes a rolling health score from 0–100, drawing on vibration amplitude, frequency spectrum drift, bearing temperature deltas, and lube oil differential pressure. A unit declining from 85 to 62 over 10 days is flagged for a work order even if no hard alarm limit has tripped. Operators see trend direction, not just point-in-time alarms.
Turn signal anomalies into readable work-order language before the technician leaves the truck
When Midstreamly detects a degradation pattern matching a known fault mode—bearing spall, impeller imbalance, seal leak, coupling misalignment—it generates a plain-English work-order recommendation. The alert names the probable failure mechanism, the specific sensor signals driving the call, and the inspection steps the technician should take. Field crews receive enough context to bring the right parts and tools on the first dispatch, reducing wasted trips.
Ingest shaft orbit and vibration data from installed Bently Nevada systems without a hardware swap
Most midstream compressor stations above 1,000 HP already have Bently Nevada 3500 Series monitoring racks installed. Midstreamly connects via OPC-UA or API bridge—pulling shaft displacement orbit data and overall vibration levels at full resolution. No new sensors required. The existing protection system certification stays intact, while Midstreamly adds a predictive analytics layer on top of the data it was already collecting.
Midstreamly integrates with your existing instrumentation and delivers fault-mode alerts inside the tools your operators already use. No new hardware. No parallel portal. No disruption to certified protection systems.
Midstreamly reads continuous vibration sensor feeds, process historian time-series from AVEVA PI System and Emerson Ovation, and Bently Nevada proximity probe data streamed from existing field instrumentation. Integration uses standard OPC-UA, REST API, or direct PI server connection—no middleware layer required on your side.
For each monitored asset, Midstreamly trains a unit-specific anomaly detection model on the first 18 months of available operating history. The model learns what normal looks like for that specific unit at that specific station—accounting for seasonal load cycles, scheduled outage patterns, and local process conditions that vary across a fleet.
A multi-variate degradation score updates every 15 minutes per unit, correlating vibration signatures, bearing temperature trends, suction and discharge pressure differentials, and lube oil quality indicators. Score trajectories are stored so operators can see whether a unit is holding steady, improving after maintenance, or accelerating toward a threshold.
When degradation scoring identifies a fault pattern, Midstreamly delivers a ranked alert into Honeywell Experion PKS or PTC ThingWorx—the operator’s existing SCADA environment—as a natural-language work-order recommendation. Supervisors see the fleet-wide risk ranking every morning. Field technicians receive specific inspection checklists before they leave the shop.
Industry average annual runtime lost to unplanned compressor and pump failures across US midstream gathering and transmission systems.
Combined lost throughput fees and expedited parts and labor per emergency shutdown event, based on midstream compression contract structures.
Midstreamly recomputes each unit's degradation score every 15 minutes from live AVEVA PI and Bently Nevada data, without requiring new field instrumentation.
Most midstream operators already have the sensor data needed to predict 60–70% of compressor and pump failures. Midstreamly surfaces those signals in real time. Request a demo to walk through what the platform would find in your existing PI or Bently Nevada data.
