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Iotron AI

Project scenario · Manufacturing & Industrial

How we would move a manufacturer from calendar-based maintenance to condition-based alerts on motors and pumps, starting with the sensors already on the line.

Typical starting point: multi-site discrete manufacturer. This scenario shows how we approach this class of problem. It does not describe a named client engagement, so no results are claimed.

Typical client
Multi-site discrete manufacturer
Industry
Manufacturing & Industrial
Built with
Iotron Sentinel · Industrial gateways · OPC UA / Modbus · MQTT
Measured against
4 agreed measures

01The challenge

Unplanned stoppages on critical rotating equipment, and maintenance that is either too early or too late.

Where we usually start

A typical starting point: some assets carry vibration and temperature sensors, PLCs log current draw, and the CMMS holds years of work orders written in free text. Each plant runs its own historian, connectivity on the shop floor is patchy, and failures on any single asset are rare — so there is little labelled failure data to train on.

02What it is measured against

How we would know it worked.

Agreed with the business before any model is built, and reported against throughout — so success is defined by the operation, not by the model.

Measures this project is reported against
#Measure
01Alert precision: share of alerts a maintenance engineer confirms as a real issue
02Lead time between first alert and the confirmed fault
03Unplanned downtime on monitored assets against the prior year
04Planner time spent triaging alerts per week

03The Iotron solution

What we build.

Edge AI on Iotron Sentinel: anomaly models that learn each asset's normal behaviour, run on gateways next to the line, and raise prioritised alerts with the contributing signals — handed straight into the existing CMMS as draft work orders a planner approves.

How the project runs

  1. Weeks 1–2

    Asset criticality review with maintenance leads; pick 20–40 assets where a stoppage hurts most and sensors already exist.

  2. Weeks 3–5

    Gateway deployment, OPC UA and Modbus connections, and a data-quality report per asset: gaps, sampling rates, sensor drift.

  3. Weeks 6–9

    Unsupervised anomaly models per asset class, tuned with maintenance engineers reviewing every alert in a shadow period before anyone is paged.

  4. Weeks 10–12

    Go-live on one plant: alerts into the CMMS, a weekly alert-quality review, and a runbook for model updates over the air.

  5. Then

    Roll out to the remaining plants and add failure-prediction models as confirmed failure events accumulate.

Technology

  • Iotron Sentinel
  • Industrial gateways
  • OPC UA / Modbus
  • MQTT
  • Time-series store
  • Autoencoder & isolation-forest models
  • Edge model compression
  • CMMS integration

04Lessons from this kind of work

What we would tell the next team.

  • Shadow-mode alerts reviewed by engineers build more trust than any accuracy figure on a slide.
  • Data quality at the sensor — mounting, sampling rate, clock sync — decides the ceiling of every model downstream.
  • Alerts that do not land in the CMMS do not get acted on; the integration matters as much as the model.
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