Conveyor motor
Data
“Vibration on motor M-07 has been rising slowly for three weeks, especially when the conveyor is loaded.”
Alert
- Likely diagnosis
- Bearing wear
- Estimated margin
- 2-4 weeks
Work order for Thursday's stop
Use case
AI predictive maintenance
Sensor data from your machines (vibration, temperature, power use) warns of wear before a breakdown. Repairs happen during a planned stop rather than in the middle of production.
- Detects wear patterns that cannot be seen with the naked eye.
- Suggests the work order with time to get the spare part.
- The maintenance team decides when and how to step in.
The problem
Repairing afterwards, or replacing parts just in case
Both usual approaches cost money. Predictive maintenance steps in when the data shows it is needed.
Reactive
- Repairs happen when the machine stops.
- The line halts without warning and production is lost.
- Urgent spare parts cost more and take longer.
Scheduled preventive
- Parts are replaced at fixed intervals, whatever their condition.
- Parts with useful life left get thrown away.
- Even so, breakdowns happen between services.
How it works
From sensor to work order
-
Sensors
Vibration, temperature, power use or pressure, from existing sensors or new ones.
Every minute, 24/7
-
Normal behaviour
The model learns how each machine runs when it is healthy.
6 months of data
-
Anomaly
It detects deviations and known wear patterns.
Bearing vibration +35%
-
Suggested order
It suggests checking the part at the next stop, with the spare part planned.
Check on Thursday
-
Human review
Maintenance decides
The technician validates, plans and logs what they found, and the model learns from it.
Bearing replaced
Examples
What can be monitored
Sample data from a fictitious plant.
Water pump
Data
“Pump B-3 uses more electricity to move the same flow as last month.”
Alert
- Likely diagnosis
- Blockage or impeller wear
- Priority
- Medium
Inspection on the next round
Compressor
Data
“The temperature of compressor C-1 rises more than usual at start-up.”
Alert
- Likely diagnosis
- Insufficient cooling
- Priority
- High
Cooling circuit check
Data sources
What your machines already record
In Microsoft environments, data can arrive through Azure IoT and be analysed in Fabric. We also work with existing SCADA systems and PLCs.
- Vibration sensors
- Temperature and pressure
- Power consumption
- PLC and SCADA
- Azure IoT and Fabric
- Breakdown history (CMMS)
Down to earth
What it takes to work
-
Continuous data
Sensors that record regularly. If there are none, you can start with one critical machine and low-cost sensors.
-
Breakdown history
Knowing what failed and when helps the model recognise patterns. Without a history, we start by detecting what is out of the ordinary.
-
The technician's judgement
A person validates every alert. Their notes are what make the model improve.
Visual guide · Predictive maintenance
Fix it
before
it breaks
Machines give warning before they break. Sensors hear it before we do, and that leaves time to plan.
01 · The P-F curve
From the first symptom to the breakdown
- 1. Vibration changes Sensors pick it up and the AI raises an alert.
- 2. Temperature or power use rises The alert is confirmed.
- 3. Noise, heat, smell Now noticeable without sensors, with little margin.
- 4. Breakdown The line stops.
02 · Three ways to maintain
When does the work happen?
Corrective
Repairs happen once the machine has stopped.
Calendar-based preventive
Parts are replaced on a schedule, even if still good, and breakdowns still happen in between.
Predictive
When it is needed
Work is done when the data says it is needed, in a planned stop.
Maintenance work Breakdown
03 · Data sources
What the sensors listen to
Machine Vibration Temperature Power use Pressure
- Your existing PLCs and SCADA
- Breakdown history (CMMS)
- On Microsoft: Azure IoT and Fabric
04 · Where to start
1 critical
machine
The one that costs most when it stops. Its data shows whether it works before scaling up.
- The technician validates each alert and decides when to act.
- Their notes are what make the model improve.
- No sensors? You can start with low-cost sensors on that machine.
Workshop
Get to the fault before the fault gets to you
With a few weeks' notice you can order the spare part, schedule the work for a planned stop and assign the right technician, without the rush.
- Fewer unplanned stops.
- Spare parts ordered in good time.
- A history for each machine of what was done and why.
Method
We start with one critical machine
-
Choose the machine
We pick a piece of equipment whose breakdown is costly and review what data it records and what breakdown history exists.
Feasibility study with real data
-
Prototype
We connect the data, train the normal-behaviour model and tune the alerts with the maintenance team.
Status dashboard and trial alerts
-
Go live
We connect the alerts to maintenance management and measure the stops avoided.
Live alerts and monthly follow-up
Related service
-
Custom AI development
We design and build artificial intelligence solutions tailored to your processes, data and tools when an off-the-shelf solution does not fit or falls short.
Related training
-
Machine learning with Python course
Real predictive models, from the data to evaluation and interpretation.
- Duration: 24 hours
- Level: Intermediate
-
AI for operations and administration course
Less paperwork and fewer repetitive tasks in internal processes.
- Duration: 8 hours (2 sessions)
- Level: Beginner
Which machine stops your production when it fails?
Tell us which equipment is critical, what sensors it has and how you log breakdowns. We will tell you whether predictive maintenance is a good place to start.