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This paper presents the application of new time frequency method, ensemble empirical mode decomposition (EEMD), in purpose to detect localized faults of damage at an early stage. EEMD is a self adaptive analysis method for non-linear and non-stationary signals and it was recently proposed by Huang and Wu to overcome the drawbacks of the traditional empirical mode decomposition (EMD). The vibration signal is usually noisy. To detect the fault at an early stage of its development, generally the residual signal is used. There exist different methods in literature to calculate the residual signal, in this paper we mention some of them and we propose a new method which is based on EEMD. The results given by the different methods are compared by using simulated and experimental signals.

The aim of this study was to investigate the effects of inter-electrode distance (IED), electrode radius (ER) and electrodes configurations on cross-correlation coefficient (CC) between motor unit action potentials (MUAPs) generated in a motor unit (MU) of parallel fibres and in a MU of inclined fibres with respect to the detection system. The fibres inclination angle (FIA) varied from 0° to 180° by a step of 5°. Six spatial filters (the longitudinal single differential (LSD), longitudinal double differential (LDD), bi-transversal double differential (BiTDD), normal double differential (NDD), an inverse binomial filter of order two (IB2) and maximum kurtosis filter (MKF)), three values of IED and three values of ER were considered. A cylindrical multilayer volume conductor constituted by bone, muscle, fat and skin layers was used to simulate the MUAPs. The cross-correlation coefficient analysis showed that with the increase of the FIA, the pairs of MUAPs detected by the IB2 system were more correlated than those detected by the five other systems. For each FIA, the findings also showed that the MUAPs pairs detected by BiTDD, NDD, IB2 and MKF systems were more correlated with smaller IEDs than with larger ones, while inverse results were found with the LSD and LDD systems. In addition, the pairs of MUAPs detected by the LDD, BiTDD, IB2 and MKF systems were more correlated with large ERs than with smaller ones. However, inverse results were found with the LSD and NDD systems.

Surface electromyographic (EMG) signals are known to be strongly in uenced by anatomical, physiological and detection system parameters. Among the detection system parameters, we are interested in the effect of muscle ber inclination on the electrode arrangement. The purpose of this study was to determine the best and the worst orientation of the electrodes arranged in nine detection systems relative to the muscleber direction and also to classify the investigated systems according to their degree of isotropy. The study was based on simulated surface EMG (sEMG) signals generated in a cylindrical multilayer volume conductor. The orientation of electrodes with respect to the ber direction was de ned by the ber inclination angle (FIA). For each detection system, the mean power (MP) of the simulated signals was computed at different FIAs and used as a basis for evaluating the effect of muscle ber inclination. We showed that for the FIA range of 0-180 , approximately isotropic systems had three positions to record sEMG signals under good conditions (MP was maximum). However, longitudinal and transversal highly anisotropic systems had two and one positions, respectively, at which sEMG signals were detected under good conditions. We showed also that the degree of isotropy of the nine detection systems investigated was less affected by the increase in muscle and fat thicknesses. However, with an increase in inter-electrode distance (IED), the degree of isotropy of approximately isotropic systems decreased while the degree of isotropy of highly anisotropic systems increased.

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