How to predict machine downtime without new sensors
Most plants already record the warning signs of a breakdown. Here’s which records to use, what patterns to look for, and how to rank machines by risk.
Pete Asman
Key takeaways
- Most plants can start predicting breakdowns from records they already keep: stoppages, fault codes, run hours and maintenance work orders.
- Breakdowns usually announce themselves through small changes, such as more short stoppages, longer cycle times or the same fault code coming back.
- Ranking machines by risk each week lets engineers go where they’re needed, instead of following fixed service intervals.
- Sensor data helps, but it isn’t the place to start: the stoppage and maintenance history is often enough to find the first patterns.
- Start with the five or ten machines whose failure costs you most, and check you have at least a year of their history.
The short answer
You can predict a lot of machine downtime from the records your plant already keeps. Stoppage logs, fault codes, run hours and maintenance work orders usually show the warning signs weeks before a breakdown. Sensors add detail later, but they aren’t the first step.
What a breakdown looks like before it happens
Most failures aren’t sudden. In the weeks before, the same machine tends to show:
- More short stoppages, often logged as minor and quickly cleared
- The same fault code coming back after it was “fixed”
- Cycle times creeping up, or output per shift dropping slightly
- More scrap or rework from that line
- Maintenance jobs taking longer, or parts being swapped more often
Each of these on its own looks like noise. Together, over a few weeks, they form a pattern.
The records you need
- Stoppage and downtime logs from your MES or production reports, with start time, duration and reason.
- Fault and alarm codes from the machine or control system.
- Maintenance work orders from your CMMS: planned and reactive, with dates, parts and labour.
- Run hours or cycle counts per machine.
- Output and quality by line and shift.
A year of this history is enough to start. Two years is better, because it shows seasonal patterns such as summer heat or winter start-ups.
From history to a risk ranking
The goal isn’t a perfect prediction for every machine. It’s a weekly list that ranks machines by how likely they are to fail soon, with the reasons behind each ranking. Your maintenance planner then decides where engineers go first.
That changes the conversation from “it’s due a service” to “it’s showing the same signs Line 3 showed before it went down in March”.
Where to start
Pick the five or ten machines whose failure costs you most, in lost output, overtime or missed orders. Check you have at least a year of stoppage and maintenance records for them. That’s your first model.
How AI Grid does it
AI Grid reads your existing production and maintenance records, finds the patterns that come before a breakdown, and ranks machines by risk each week. It explains each ranking in plain English and forecasts demand and capacity alongside it, so you can plan maintenance around production.
See it on data like yours. Book a 30-minute demo, or read more on our Manufacturing page.
