AI Predictive Maintenance

Anticipate failures 7–30 days ahead and eliminate unplanned downtime

WhatsApp
Agent sheet
Category
Support and customer service
Model
open, installed on your premises
Data
never leaves
Subscription
€0 per month
01Overview

An unplanned failure on a production line costs an average of €260,000 per hour in production losses, emergency repairs and client penalties. Scheduled preventive maintenance wastes resources on equipment that doesn't need it. AI predictive maintenance continuously analyzes vibration, temperature and electrical consumption signatures of your equipment to detect failure precursors before they occur.

The problem

Corrective maintenance costs 3–5× more than preventive maintenance. Preventive maintenance replaces still-healthy parts. Without prediction, unplanned downtime disrupts production, client deadlines and team safety. And technicians spend time on repetitive manual inspections.

Our answer

Vibration, temperature and current sensors are connected to critical equipment. Data is continuously collected via the IoT pipeline and analyzed by ML models (isolation forest, LSTM, XGBoost) trained to recognize precursor signatures of failures specific to your machines. An alert is generated 7–30 days before the predicted failure, with the probable defect type and recommended urgency.

02How it works

Live in four steps.

  1. 01
    Critical equipment identification

    Criticality analysis of each piece of equipment (production impact, replacement cost, historical MTBF). Sensor deployment prioritization.

  2. 02
    Sensor & IoT pipeline deployment

    Vibration, thermal and electrical sensor installation. Real-time IoT pipeline connection via MQTT or proprietary protocol.

  3. 03
    Predictive model training

    Using historical failure data to train anomaly detection and RUL (Remaining Useful Life) prediction models.

  4. 04
    Alerts & CMMS integration

    Predictive alert generation with defect type, urgency and recommended intervention. Integration with your CMMS (SAP PM, Maximo, Fiix).

03What you gain

Results you can measure.

Failures anticipated 7–30 days ahead

Models detect precursor signatures well before failure. Your maintenance team plans ahead.

Unplanned downtime reduced by 70%

With early alerts, emergency shutdowns become planned stops. Production is no longer disrupted.

Maintenance costs -35%

Less replacement of healthy parts, fewer costly emergencies, optimized spare parts inventory.

04Frequently asked

What we get asked before signing.

Which equipment types benefit from predictive maintenance?
Electric motors, pumps, compressors, bearings, turbines, conveyors, rotating equipment in general. Also applicable to thermal and electronic equipment.
How much historical failure data is needed?
Ideally 12–24 months with some recorded failure events. Transfer learning techniques enable starting with less data.
Does the solution integrate with our CMMS?
Yes. We have connectors for SAP PM, IBM Maximo, Fiix, UpKeep and major CMMS systems. Integration can automatically create work orders.
Is prediction accuracy guaranteed?
We target a detection rate > 85% with a false positive rate < 10%. Real performance is measured and communicated during validation.

Let's talk about your case for thirty minutes.

A conversation to understand your context, volumes and tools. You leave with a scope and an order of magnitude, no commitment.

WhatsApp
+261 38 66 261 00team@wikolabs.com