Predictive Maintenance
Predict equipment failure before it stops the line - remaining-useful-life estimates your planner can actually schedule around.
TELEMETRY - PUMP P-12 · LIVE
Sensor traces - last 30 days
VIBRATION RMS mm/s
BEARING TEMP °C
MOTOR CURRENT A
⚑ vibration trend +34% · bearing-wear signature
Asset health - P-12
bearing wear · conf 0.91 · failure mode known
RUL ≈ 18 days P10-P90: 9-31 d
How it works
Connect the sensors you have
Vibration, temperature, current, PLC tags - via your historian or OPC-UA. No new hardware required to start; we tell you if it's worth adding.
Learn each asset's normal
Baselines per machine and per operating mode - so a heavy-load Monday doesn't look like a failure and real wear doesn't hide in the noise.
Estimate remaining useful life
Named failure mode, confidence, and an honest P10-P90 window - a range your planner can schedule around, not a fake precise date.
Turn warnings into work orders
Draft work orders into your CMMS with parts and window suggested; planner approves. Every alert and outcome logged for the loop.
Methodology reference
The published method this build is based on, and what it did in production.
Shell scaled predictive maintenance to 10,000+ assets, running ~11,000 models to catch failures before they happen.
Fleet-scale failure prediction · as reported by C3 AI ↗
Referenced for methodology only. AimRank is not affiliated with, endorsed by, or a customer of these companies; each links to its own public source. Sources last checked September 2026.
The Four Guarantees™ - this build
The scaffold wires all four in. The PoC proves them on your data, as of that day. Only Implementation keeps them true over time: watched, drift-checked, with recourse.
Measured value
~40% of failure-mode downtime converted to planned maintenance at reference fleet; eval gate: precision/recall per failure mode on your history.
Defensible
Every alert carries the signature, confidence and sensor evidence behind it; alert-to-outcome log; no black-box "health scores".
Self-improving
Confirmed and missed failures retrain per-asset baselines; alert precision tracked monthly; sensor drift and dead channels flagged.
Yours & everywhere
Runs on-prem or in your cloud, next to the historian. Full source. MCP endpoint - your CMMS and copilots read fleet health directly.
The number, sized honestly
Reference buyer: Lithuanian manufacturer - 30-80 critical rotating assets, an existing historian, unplanned downtime costing €2-10k per hour.
Three ways to own it
| Tier | What you get | Price | |
|---|---|---|---|
| Scaffolding ★ | The full repo - telemetry pipeline, per-asset baseline models, RUL estimator with intervals, alert-to-work-order flow, MCP server. Reference run on public bearing datasets. | €2,490 | |
| PoC ★★RECOMMENDED | Trained on your historian data - failure-mode precision/recall and warning horizon measured on your fleet's own history. Code + quality report yours. No guarantee until your sensors prove the signal exists. | from€7,500 | |
| Implementation ★★★ | Production: live alerts into CMMS work orders, per-asset baselines maintained, sensor-drift monitoring, retraining on confirmed outcomes, monthly downtime report - the agreed number (conservative) attaches here. | from €22,500+ from €900/month assurance |
★ = engagement depth. PoC is the recommended path: quality proven on your data before production money. The PoC carries no performance guarantee by design; the agreed number (conservative) attaches at Implementation, informed by the PoC report.
What we don't promise
Ready to see your own number?
Request the build: within 48h you get a personal reply with the value sized to your volume.
No commitment · reply within 48h · your data stays in the EU