Mia Loccufier

TL;DR: A feature learning model for condition monitoring based on convolutional neural networks is proposed to autonomously learn useful features for bearing fault detection from the data itself and significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier.

TL;DR: In this article, a technique is presented for the detection and localization of an open crack in beam-like structures using experimentally measured natural frequencies and the Particle Swarm Optimization (PSO) method.

TL;DR: A novel automatic fault detection system using infrared imaging, focussing on bearings of rotating machinery, able to distinguish between all eight different conditions with an accuracy of 88.25%.

TL;DR: This paper presents a methodology for determining simple operating strategies for anaerobic digestion systems characterized by a two populations model that allows to select appropriate inputs and initial states to achieve a good operation of the process.

TL;DR: A complete implementation of a nonlinear energy sinks is presented, from design and practical realization, to verifying the performance measures experimentally, validates the ease of use of the performance Measures and their ability to predict experimental performance of a NES mitigating multi-modal vibrations.