Predict equipment failure before downtime hits.
Sensor-driven analytics for plant equipment. Both the model and the telemetry stay on customer infrastructure, with evidence trails operational and safety reviewers can use.
// before downtime hits · inside the perimeter
Unplanned downtime is expensive and avoidable.
Traditional CMMS schedules maintenance on time intervals, not on actual asset condition. The result is a mix of unnecessary maintenance on healthy assets and missed warnings on degrading ones. Unplanned downtime cascades through the line and the supply chain.
Cloud-based predictive maintenance creates a separate sovereignty problem. Sensor histories are production-sensitive, vendors expect outbound telemetry, and the operations team loses control of when a failure prediction reaches the floor.
Condition-based maintenance, governed end to end.
Limit Signal: Maintenance ingests vibration, temperature, current, and acoustic signals from existing instrumentation, models normal vs degrading operation, and surfaces failure predictions with the evidence behind them.
- 01
Multi-modal anomaly detection per asset
Models learn the normal operating envelope for each asset from its own history, not from a generic reference. Vibration spectra, temperature trends, and acoustic patterns are analyzed together, not in isolation.
- 02
Failure-mode prediction with confidence and lead time
Predictions name the likely failure mode (bearing, alignment, lubrication, electrical) with a confidence score and an estimated lead time. Maintenance planners get actionable detail, not a binary alarm.
- 03
Work-order integration with CMMS and ERP
Predictions surface as proposed work orders in the existing maintenance system. Operators accept, defer, or override; every decision is logged with the predicted lead time and the actual outcome.
- 04
Evidence that closes the audit loop
Every prediction, every operator decision, and every subsequent observation lands in the evidence store. Root-cause reviews and safety audits reconstruct the data the system saw, not just the summary it surfaced.
On premise. On your hardware.
Limit Signal: Maintenance runs in the customer data center or at the plant edge. Sensor data, historian databases, model weights, and the evidence trail all stay inside the customer perimeter. No data is transmitted off-site by default.
The application integrates with existing historians (PI, Aveva, GE), CMMS platforms, and ERP work-order systems. The operations team keeps the interfaces they already use; the predictions flow into the workflow they already have.
- Where it runs
- Plant data center or edge cluster, sized to the asset count and sensor frequency. Multi-site deployments coordinate via the customer WAN.
- What stays on premise
- Historian feeds, sensor data, model weights, failure-mode predictions, evidence records, and operator-response history.
- Who controls the data
- The customer is the controller. Limit Systems is a processor under the per-deployment DPA. Telemetry to Limit Systems is opt-in only.
- Evidence for safety reviewers
- Per-prediction records with input signals, model version, confidence, decision, and outcome. Format matches the customer's existing reliability-engineering review process.
Every application runs on the same sovereign platform. The identity layer, the audit trail, the policy engine, the evidence retention. Built once, used by all. Explore the platform →
- Maritime · Offshore
Predictive maintenance for fleets
AI maintenance for ships at sea, where cloud connectivity is unreliable and the regulator wants every decision auditable. The full inference loop runs on-vessel.
- Factories · Manufacturing
Industrial coatings inspection
Real-time defect detection and yield analytics for paint and coating production, deployed on the factory floor where regulators want every decision auditable.
See it in your environment.
Walk us through your perimeter, your evidence requirements, and the systems already in place. We'll show you how the deployment looks.