INDUSTRY & MAINTENANCE
Equipment downtime prediction: know how long it will last
Equipment downtime prediction with TIMi estimates, once a risk or an incident is identified, how long an equipment will be down and the impact on output, and shows the factors behind the estimate.
Duration estimate
For a stop that is anticipated or already under way.
Cause and context
Type of failure, spare part availability and complexity of the intervention.
Explained estimate
Each estimate shows what lengthens the stop.
HOW IT WORKS
How equipment downtime prediction works.
Three steps, in one engine, on a standard server with no cluster.
1. Prepare the data
Anatella gathers the history of stops already observed (cause, duration, resources mobilized) and the conditions in which they happened, then structures them into variables.
2. Build the model
Modeler estimates the probable duration of a stop from its identified cause and the operating context, based on comparable stops already observed.
3. Estimate and explain
Each stop gets a duration estimate with the factors behind it, such as the type of failure, the availability of the spare part or the complexity of the intervention.
Anatella
ModelerHOW IT DIFFERS
After the risk, the duration.
Knowing a stop will happen is half of the question. The other half is how long it lasts.
Failure first, duration next
Failure prediction estimates if and when an equipment may fail. Downtime prediction comes once the risk is identified or the incident has happened, to estimate how long the equipment will be unavailable. The two complete each other in a full predictive maintenance setup.
A detailed history is needed
The precision of the estimate depends directly on the quality and detail of the history of stops and interventions already carried out.
From duration to output impact
If production data is available (pace, dependencies between equipment), the model can be extended to estimate the overall impact, not only the technical duration of the stop.
EXPLAINABILITY
A duration says how long. The factors say what to act on.
An estimated duration alone gives a probable delay, not what determines it. A white-box model shows the factors in the estimate, so planning teams know whether the delay comes from a spare part, the complexity of the intervention or the availability of the teams, and can act on the limiting factor.
For production teams
A reliable duration estimate lets you replan the workload or mobilize a workaround with full knowledge.
For maintenance teams
Identifying what lengthens a stop (part, resource, complexity) helps to act on it for future stops.
For model owners
Modeler’s built-in Drift Report shows when a model loses accuracy, so you update it when needed instead of on a fixed calendar.
DATA
What data a downtime model uses.
Anatella connects to more than 80 sources, so this data can be gathered without prior custom development.
Stop history
Cause, actual duration, resources mobilized.
Parts and teams
Availability of spare parts and intervention teams.
Intervention type
Typical complexity by cause of stop.
Operating conditions
Conditions at the time of the stop.
PROOF
Closest references.
We do not yet have a published customer story dedicated to downtime duration. These two deployments show what is at stake: very long shutdowns and stopped production lines.
Well shutdowns of up to 6 months
Pump failures in remote sites caused very long shutdowns. Failure prediction reduced downtime, with gains in productivity and safety.
One failing machine stops the whole line
Predicting failures 30 minutes ahead lets technicians intervene proactively on machines that run 24/7.
FAQ
Common questions.
Do we need a detailed history of past interventions?
Yes. The precision of the estimate depends directly on the quality and detail of the history of stops and interventions already carried out.
Can it estimate the impact on output, not only the duration?
Yes, if production data is available (pace, dependencies between equipment), the model can be extended to estimate the overall impact, not only the technical duration of the stop.
Does it apply to planned stops too?
Yes. The same principle can refine the duration estimate of a planned maintenance, based on the history of similar interventions.
Do we need it on top of an existing predictive maintenance setup?
It is complementary: predictive maintenance anticipates that a stop will happen, this model helps to anticipate its duration and impact once the risk is identified.
RELATED USE CASES
Keep exploring.
Equipment Failure Prediction
Estimate the probability that an equipment fails at a given horizon, and why.
Predictive Maintenance
Anticipate equipment failures from the data you already collect, with the reasons.
Production Yield Optimization
Find which process parameters drive the share of conforming parts.
SEE FOR YOURSELF
Test it on your own data.
Run TIMi on your own data with a free trial, or talk to us about your case.
