XTRACTIS FOR predictive maintenance

Prediction of the Rupture of a Flexible Underwater Pipe

Benchmark vs. Logistic Regression, Random Forests, Boosted Trees & Neural Networks

How to successfully predict the rupture of underwater pipes given the apparent complexity of the phenomenon?

Goals & benefits

Identify the predictors involved in the rupture of a pipe and enhance technical knowledge by helping petroleum industry engineers understand the causal relationships between these predictors, their combination, and the rupture.

Find the truly influential parameters for assessing the state of the pipe and thus reduce measurement and maintenance costs.

Carry out maintenance action specific for each pipe in order to avoid critical damage, thanks to rapid and transparent decisions.


We get a Predictive Model that is:


A Decision System composed of 27 unchained gradual rules, each rule using some of the 20 variables that XTRACTIS identified as significant (out of 74 pipe characteristics).


Good Real Performance on External Test.


Running in real-time up to 70,000 predictions per second (i7 @2.5GHz with 8 physical cores), offline or online (API).

Use Case 2023/01 (v1.0)

Results by
XTRACTIS® GENERATE 12.2.43841 (2023/01)


  1. Problem Definition
  2. Xtractis Solution
  3. Top-Model Induction
  4. Explained Predictions for 2 cases
  5. Top-Models Benchmark