OSREP edge units mount on your existing equipment, read every sensor already wired there, and return one thing: a continuous state — nominal, degrading, or fault — with the exact input that drove the change. No cloud. No data egress. No waveforms to read at 2 a.m.
Every plant has the sensors. Every historian has the data. The gap is between raw signal and operational decision — and the existing options ask you to give away your data, sign a five-year contract, and hope someone in another state notices when your bearing starts to fail.
Terabytes of vibration, acoustic, and process data sit in storage. Your reliability team can't review it all. Your operators don't have time. By the time someone notices a trend, the failure already happened.
Their model runs on their servers, on your operating data, behind their paywall. Cancel and you walk away with nothing. The architecture is built to keep you locked in, not to keep your equipment running.
Another tool, another login, another wall of charts. The signal is buried in the noise. What gets actioned is what's clear at a glance — a state that says nominal, degrading, or fault, and which sensor drove the change.
Three steps. All intelligence stays at the asset. What comes back is a state, not a firehose.
An OSREP edge processing unit mounts at each monitoring point. It connects to whatever sensors are already wired there — no rip-and-replace. Handles acoustic, vibration, thermal, electrical, and process inputs natively.
After a short commissioning window, the unit has modeled this specific asset at this specific point. Learning happens entirely on the hardware — no data leaves, no cloud loop. Identical units produce different models on different machines.
From that point on, the unit monitors all sensor inputs and emits a single state: nominal, degrading, or fault — with confidence and time-in-state. Your operators get signal, not noise.
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The unit is sensor-agnostic. Our core work is acoustic and vibration — but the on-unit architecture ingests any signal with a stationary baseline. You bring the sensors already on your equipment; we bring the intelligence layer on top.
Failure signatures in the ultrasonic band — bearing friction, cavitation, turbulence, valve chatter, electrical arcing. Often the earliest indicator of mechanical degradation.
Mechanical signatures of rotating and reciprocating equipment — imbalance, misalignment, bearing race defects, gear-mesh faults, looseness, resonance. The unit learns each machine's harmonic fingerprint.
Bearing hot-spots, motor winding rise, heat exchanger fouling. Thermal inputs contextualize acoustic and vibration — a bearing fault reads differently at 40°C vs 110°C.
Line pressure, differential pressure, flow rate. Cavitation, blockage, and valve wear all present in process data before they escalate to mechanical failure.
Motor current signature analysis, power factor, voltage imbalance. Rotor bar faults, coupling issues, and bearing wear all leave electrical traces the unit learns to read.
Most industrial sites already have instrumentation running to PLCs or historians. The unit speaks Modbus, HART, OPC-UA, and MQTT natively — so states drop into your existing SCADA, historian, or operations platform without a new integration project. Custom protocols handled in commissioning.
Raw sensor data stays on the unit. What travels upstream is already interpreted — actionable without further processing.
Every unit emits a rolling state: nominal, degrading, or fault — with confidence value and time-in-state. Updates in real time. If it hasn't changed, you don't need to do anything.
When state changes, the unit tells you which sensor drove it and by how much. Cause, not just symptom — bearing acoustic up 40%, temperature nominal, vibration nominal. That's a bearing starting to go.
Deploy across multiple points per asset or an entire fleet. Your operations layer sees a clean grid of states — not a wall of waveforms nobody has time to read.
Tell us what you're monitoring. We'll come back with a fit assessment and what deployment looks like at your site.
Cloud-based predictive maintenance asks you to ship operating data offsite, run inference on someone else's servers, and trust them to give it back if you ever leave. The architecture wasn't designed for industrial reality. OSREP was.
If your CISO has ever asked "where exactly does the data go?" — this is the answer that ends the conversation.
One unit. Every sensor. The only output that matters.
We'll come back with a short, specific assessment — whether OSREP is a fit for your assets, what a deployment looks like at your site, and which of your existing sensors we'd work with.