INDUSTRY & MAINTENANCE
Equipment failure prediction: a probability per machine, with its causes
Equipment failure prediction with TIMi estimates the probability that an equipment fails at a given horizon, from its operating data, and shows the factors behind the score.
Probability per equipment
At a horizon you define with the technical teams.
One model per equipment type
Warning signals differ from one type of equipment to another.
Explained per variable
Each probability comes with the factors that weigh in.
HOW IT WORKS
How equipment failure prediction works.
Three steps, in one engine, on a standard server with no cluster.
1. Prepare the data
Anatella gathers the operating data of the equipment and the history of failures already observed, then structures them into variables.
2. Build the model
Modeler estimates, for a given equipment and a defined horizon, a probability of failure from the signals observed.
3. Score and explain
Each equipment gets a probability of failure with the factors behind it, variable by variable.
Anatella
ModelerHOW IT DIFFERS
Failure prediction and predictive maintenance.
One is the building block, the other is the process around it.
A signal, then an action
Failure prediction produces a signal: the probability that an equipment fails at a given horizon. Predictive maintenance is the wider process that uses this signal to plan an intervention (when, on which part, with which resources).
Pick a realistic horizon
It depends on the type of equipment and on how fast a warning signal turns into a failure. At SANY, critical engine failures were anticipated within the hour. The horizon is defined with the technical teams.
One model per equipment type
A separate model is usually built for each type of equipment, because the warning signals differ from one to another.
EXPLAINABILITY
A probability says how likely. The factors say what kind of risk.
A failure probability alone gives a level of risk, not what drives it. A white-box model shows the factors in the score for this equipment, which helps to tell normal wear from a recent operating anomaly.
For technical teams
The nature of the risk identified directly guides the type of check to perform.
For prioritizing resources
When several machines show a high risk, understanding the cause helps to decide which one to handle first.
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 failure model uses.
Anatella connects to more than 80 sources, so this data can be gathered without prior custom development.
Sensor and operating data
Temperature, vibration, pressure, depending on the equipment.
Failure history
Failures already observed.
Operating conditions
Load, pace, environment.
Age and maintenance
Age of the equipment and maintenance history.
PROOF
Failure prediction in production.
SANY and GX Labs are our references for predicting failures on heavy equipment.
AUC above 98% with only 107 historical failures
The model pinpointed the temperature thresholds that led to an engine shutdown within the hour, enabling immediate preventive action.
Pump failures predicted on 5,000 wells
Models on 500 sensors and 4,000 derived variables support early failure detection and real-time weak signal analysis.
FAQ
Common questions.
Is this different from predictive maintenance?
The modeling principle is the same. The distinction is the use: this use case is about the risk signal produced by the model, while predictive maintenance is the whole process that uses this signal to act.
Does it work for an equipment that has never failed?
It is harder without any failure history, but a model can rely on comparable equipment already monitored to build a first estimate.
What prediction horizon is realistic?
It depends on the type of equipment and on how fast a warning signal evolves into a failure. The horizon is defined with the technical teams, depending on the context.
Can one model handle several types of equipment?
A separate model is usually built for each type of equipment, since the warning signals differ from one equipment to another.
RELATED USE CASES
Keep exploring.
Predictive Maintenance
Anticipate equipment failures from the data you already collect, with the reasons.
Equipment Downtime Prediction
Estimate how long a stop will last and what it will cost in output.
Asset Degradation Modeling
Model how the condition of an asset evolves with use, and estimate its remaining life.
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.
