Mohammed El Badaoui

This paper brings a practical solution to the problem of spectral peak detection in non-uniform spectra. It applies a robust probabilistic approach that fits the histogram of trimmed spectral data with a truncated Gamma distribution. The estimated distribution parameters are used to derive a threshold through a hypothesis test on the presence of peaks. The proposed approach gains its robustness from the formulation of the no-peak distribution, while no knowledge is available about the amount of peaks in spectral data. The authors propose a pre-processing step to cope with a non-uniform spectrum. The proposed methodology is validated on both simulated and experimental vibration and acoustic signals.

Gait, which is the scientific term for walking, is one of the most important activities of daily life for humans. Gait performance is used as a general measure of functional ability, health, and well-being of individuals. Gait analysis using kinetic data such as pressure distribution underneath the foot has been a topic of interest for assessing falls in elderly and certain

The compressive sensing (CS) of mechanical signals is an emerging research topic for remote condition monitoring. The signals generated by machines are mostly periodic due to the rotating nature of its components. Often, these vibrations witness strong interactions among two or multiple rotating sources, leading to modulation phenomena. This paper is specifically concerned with the CS of this particular class of signals using a Bayesian approach. The main contribution of this paper is to consider the particular spectral structure of these signals through two families of hierarchical models. The first one adopts a block-sparse model that jointly estimates the sparse coefficients at identical or symmetrical positions around the carrier frequencies. The second is a spike-and-slab model where the spike component takes into account the symmetrical properties of the support of non-zero-coefficients in the spectrum. The resulting posterior distribution is approximated using a Gibbs sampler. Simulations show that considering the structure in the prior model yields better noise shrinkage and better reconstruction of small side-bands. Application to condition monitoring of a gearbox through CS of vibration signals highlights the good performance of the proposed models in reconstructing the signal, offering an accurate fault detection with relatively high compression rate.

In this paper, we propose a novel fatigue assessment technique using Vertical Ground Reaction Force (VGRF) signals acquired during ultra-marathon running. To achieve that goal, we performed frequency-domain on singlechannel VGRF signals on different time intervals during ultra-marathon running and tracked the features' variation. Our method was applied on single-channel VGRF signals recorded via instrumented treadmill during a 24-hour ultramarathon ran by 12 well-trained athletes. The purpose behind our work is to develop computationally-efficient techniques for real-time fatigue assessment during running activities, taking advantage of the low level of complexity and deterministic behavior of the signals in hand when compared to other fatigue indicating electrophysiological signals.