PREDICTIVE MAINTENANCE SOFTWARE
Not cancelled. Muted. By the operators it was supposed to help. Here's why — and what it takes to build one they actually trust.
HOW THE MARKET DOES IT
Trained on “motors in general,” not YOUR motor. Your press has had a factory quirk since day one — so the software flags it every single day. Real drift, meanwhile, hides inside the same generic band.
A model that doesn't know your machine can only be wrong about it.In what unit? From which evidence? With what uncertainty? Most tools can't answer. The number exists because it demos well.
A prediction without provenance is a prop.Gateways, per-point subscriptions, installation windows — months of cost before the first insight. The economics are backwards, and the pilot budget dies before the value shows up.
You're asked to pay for instruments before anyone reads the ones you have.A red icon on a dashboard is not a maintenance window. If the prediction doesn't change the production plan, a human still does that work — usually too late, usually in Excel.
Visibility that changes nothing is decoration.THE MARKET SELLS PREDICTIONS.
WHY THE ATHERYA WAY WINS
Atherya learns each machine's behavioral signature — per machine, per recipe, per season — from your own history. In one real plant: 141 machine × recipe signatures from six years of existing data, including a factory defect discovered between two twin presses. Same brand, same model, different DNA. For generic software that defect is a daily alarm; for Atherya it's a signature: it adapts to the personality and alarms only on real drift.
A remaining-useful-life figure appears only when the model's output is verifiably in hours, with uncertainty attached. When Atherya doesn't know, the screen says “not measured.” No estimate without a source — it's the rule across the entire product.
Atherya reads what your machines already write: SQL historians in any dialect, OPC-UA direct from SCADA/PLC, CMMS and ERP. Six million readings across 42 machines were read in that plant before the software was even installed. Add sensors later, only where evidence says they pay.
Atherya's prescriptive engine proposes the intervention with its hours and the deliveries it protects — and reserves the window inside the production schedule itself. Maintenance stops arguing with planning; it becomes part of the plan.
PREDICTIVE MAINTENANCE SOFTWARE
Most predictive maintenance software watches a sensor and raises an alarm. Atherya's predictive maintenance software starts from context: it learns each machine's normal per product, season and restart from the data you already have, separates real degradation from harmless anomalies, and books the intervention straight into the production plan.
SAME MODEL. DIFFERENT DNA.
Illustration of a real case: a factory defect found between twin presses. The offset is the machine's personality; only movement beyond ITS band is a problem.
If the answers take a slide deck, you already have your answer.
Onboarding results from one real plant. Scenario figures on this site are declared as scenarios, never measurements.
QUESTIONS, ANSWERED
No. Atherya starts from data your machines already produce — SQL historians, OPC-UA from SCADA/PLC, CMMS and ERP records. Hardware comes later, only where evidence says it pays.
Thresholds compare your machine to a generic limit. Atherya compares it to its own learned signature — so a chronic personality never alarms and a real drift never hides.
The figure appears only when the trained model's output unit is verifiably hours, with uncertainty. Otherwise: “not measured.” Never a guess.
Onboarding reads your history first — you get the story of your plant (bottleneck, hidden capacity, machine behavior) before installing anything.
YOUR PLANT · YOUR DATA · THE PROOF
Send the raw data your machines already produce. We hand you back your plant's story — the bottleneck, the hidden capacity, the machine with a personality nobody catalogued. If none of it surprises you, you've lost thirty minutes. That's the entire risk.
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