Liebherr, a global leader in the manufacturing of engines and construction machinery, is launching the “Enhanced Anomaly Detection within Internal Combustion Engines” project, funded by InnoSuisse. This initiative aims to optimize engine maintenance using artificial intelligence, focusing on predicting the Remaining Useful Life (RUL) of engine filters.

 

The goal is to detect when engine filters need to be replaced before they become clogged, using data from engine sensors. By leveraging locally deployed models, the project ensures data confidentiality, reliability, and cost efficiency while improving operational performance.

 

This project investigates the opportunities offered by machine learning (ML) and deep learning (DL) for analyzing time series data to enable predictive maintenance. Some research questions are also explored as: How can predictive maintenance revolutionize the way engines are managed? What role will artificial intelligence play in enhancing sustainability and reducing downtime in industrial applications?

 

This project seeks to explore these questions while driving forward Liebherr’s commitment to innovation.