Mohamed El Badaoui

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We present in this paper the detection of a rolling defect in an asynchronous machine by analysis of the electric signals. For this purpose, we used a Wiener filter to decrease the dynamics of the 50 Hz and to increase the frequencies associated to the mechanical load. Thus, we could detect the presence of a ball defect. This result is corroborated by an(More)
In last decades, a well-studied characteristic of signals called Cyclostationarity (CS) has provided very important survey, highlighting the impact of CS models on signal analysis in telecommunication, mechanical, acoustic, biomechanical and econometric signals. It is a technique that offers diagnostic advantages for the analysis of failures, faults and(More)
We present in this paper the detection of a rolling defect in an asynchronous machine by analysis of the electric signals. For this purpose, we used a Wiener filter to decrease the dynamics of the 50 Hz and to increase the frequencies associated to the mechanical load. Thus, we could detect the presence of a ball defect. This result is corroborated by an(More)
In this work we present a new techniques to detect a mechanical faults in asynchronous machines by exploiting the instantaneous frequencies estimated starting from an accelerometer sensor, optical encoder and stator current. Bearing damages cause a torque oscillations as well as a disturbance in the velocity signal which can be detected by a mechanical(More)
This paper addresses the frequency content of Vertical Ground Reaction force (VGRF) signals. Signal processing of such data is crucial in order to characterize the gait itself. The outcome processing can be used to show the presence of disease, to track the rehabilitation or even to detect the possibility of falling. In order to come up with the correct(More)
This paper presents a new technique for the blind separation of cyclostationary signals by exploiting the cyclostationary nonstochastic temporal-probability models (fraction on time FOT) for signals (time-series) with periodic structure. The proposed approach is based on the joint diagonalization nonorthogonal of a set of matrices which have the same(More)
The detection of chatter is crucial in machining process and its monitoring is a key issue, so as to insure a better surface quality, to increase productivity and to protect both the machine and the workpiece. An investigation of chatter monitoring in high speed machining process on the basis of cyclostationary analysis of the instantaneous angular speeds(More)
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This paper suggests an automated approach for fault detection, diagnosis and identification of roller bearings, which is based on optimized form of growing neural networks. In our recent work, we selected features according to their classification accuracy within supervised learning stage. Since each one of selected features has different effect on(More)
Gear vibration signals can be modeled as amplitude modulation and frequency modulation (AMFM) processes, and have cyclostationarity, so cyclic spectral analysis is used to extract the modulation features of gearbox vibration signals, and to detect and assess localized gear damage. The cyclic spectral density of AMFM signals is deduced, and its properties in(More)