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How to successfully predict the functional degradation of a naval propulsion unit compressor, given the hyper-complexity of the phenomenon (strongly nonlinear behavior)?
Allow business experts and maintenance managers to understand the causal relationships between some turbine parameters and its future state of degradation.
Find the truly influential parameters for assessing the state of degradation and thus reduce measurement and maintenance costs.
Carry out maintenance actions specific to each turbine upstream in order to avoid critical damage, thanks to rapid and systematic diagnostics, while justifying each intervention.
We get a Predictive Model that is:
Intelligible.
A Decision System composed of 428 unchained gradual rules (aggregated into 36 disjunctive fuzzy rules). Each rule uses some of the 12 variables that XTRACTIS identified as significant (out of 14 Potential Predictors that are turbine parameters).
Excellent Real Performance on the Test dataset.
Efficient & Operational.
Running in real-time up to 70,000 predictions per second (i7 @2.5GHz with 8 physical cores), offline or online (API).
Use Case 08/2022 (v4.2)
Results by
XTRACTIS® GENERATE 12.2.43064 (08/2022)
DOCUMENT CONTENTS
INTELLITECH develops and publishes a state-of-the-art no-code artificial intelligence solution replicating human reasoning using continuous logic-based mathematics and implementing collective and evolving learning algorithms.
Our XTRACTIS sovereign and unique solution has multisector applications. It provides our clients with reliable and transparent Ai-driven decision-making for critical and strategic processes.