Pedro Jover

Numerical magnetic field analysis and signal processing for fault diagnostics of electrical machines

Numerical magnetic field analysis is used for predicting the performance of an induction motor and a slip‐ring generator having different faults implemented in their structure. Virtual measurement data provided by the numerical magnetic field analysis are analysed using modern signal processing techniques to get a reliable indication of the fault. Support vector machine based classification is applied to fault diagnostics. The stator line current, circulating currents between parallel stator branches and forces between the stator and rotor are compared as media of fault detection.

Signal processing of vibrations for condition monitoring of an induction motor

Vibration monitoring is studied for fault diagnostics of an induction motor. Several features of vibration signals are compared as indicators of broken rotor bar of a 35 kW induction motor. Regular fast Fourier transform (FFT) based power spectrum density (PSD) estimation is compared to signal processing with higher order spectra (HOS), cepstrum analysis and signal description with autoregressive (AR) modelling. The fault detection routine and feature comparison is carried out with support vector machine (SVM) based classification. The best method for feature extraction seems to be the application of AR coefficients. The result is found out with real measurement data from several motor conditions and load situations.

Electromagnetic flux-based condition monitoring for electrical machines

The main aim of this paper is to study the ability of various electromagnetic fluxes to enhance the detection and localization accuracy of faults in a 35 kW cage-induction motor. Another aim of this paper is to study the modifications brought by various stators winding design to some of the asymmetrical air-gap electromagnetic flux density harmonics responsible for the detection of different faults. For this purpose, the following designs are considered: stator winding formed of no parallel branches, 2 and 4 parallel branches respectively. For the design consisting of no parallel branches we have considered the stator winding formed of one and two layers respectively. The studied faults consist of inter-turn short circuit in the stator winding, rotor-cage related fault (bars and end-ring breakage), eccentricities (static and mixed) and bearing failure. The relevant fault signatures of the electromagnetic fluxes are issued both from measurements and from two-dimensional numerical electromagnetic field simulations at steady state. When possible, the experimental verification for the ldquovirtualrdquo measurement signals provided by the numerical electromagnetic field simulations is achieved.

Complementary diagnosis of rotor asymmetries through the tracing of the Right Sideband Component in the stator startup current

In this paper, the tracing of the right sideband component (RSC) evolution in the stator startup current is proposed for the diagnosis of rotor asymmetries in induction machines. Although several works have dealt with the detection of the left sideband component (LSC) during the transient, few contributions have focused on the RSC, perhaps due to its more difficult detection, since it has often lower amplitude. In this work, several signal processing techniques, such as the short time Fourier transform (STFT), the discrete wavelet transform (DWT), the continuous wavelet transform (CWT) and band pass filtering are applied in order to extract this component during the transient. Several experimental startup current signals are used for this purpose. The results show that the transient extraction of the RSC might constitute an additional source of information which could enable a more reliable diagnosis of the fault, mainly in those cases in which the transient LSC evolution could be partially masked by other phenomena.