Marian Negrea

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.

Fault diagnostics of an electrical machine with multiple support vector classifiers

Support vector machine (SVM) based classification is applied to fault diagnostics of an electrical machine. Numerical magnetic field analysis is used to provide virtual measurement data from healthy and faulty operations of an electric machine. Power spectra estimates of a stator line current of the motor are calculated with Welchs method, and SVMs are applied to distinguish the healthy spectrum from faulty spectra. Multiple SVMs are combined with a majority voting approach to reconstruct the final classification decision.

Support vector classification for fault diagnostics of an electrical machine

Support vector classification (SVC) is applied to fault diagnostics of an electrical machine. Numerical magnetic field analysis is used to provide virtual measurement data from healthy and faulty operation of an electrical machine. Power spectra estimates of the stator current of the motor are calculated with Welchs method, and SVC is applied to distinguish healthy spectrum from faulty spectra. Results are promising. Most of the faults can be classified correctly.

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.

A general scheme for induction motor condition monitoring

This work proposes a general scheme to detect induction motor faults by monitoring the motor current. The scheme is based on signal processing (predictive filters), soft computing techniques (fuzzy logic), the analytical studies of induction motor under fault conditions and the analysis of data generated by finite element method (FEM). The predictive filter is used in order to separate the fundamental component from the harmonic components. Fuzzy logic is used to identify the motor state and FEM is utilized to generate virtual data. A simple and reliable method for the detection of stator failures based on the phase current amplitudes is implemented and tested. The layout has been proved in MATLAB/SIMULINK, with both data from FEM motor simulation program and real measurements. The proposed method is simple and has the ability to work with variable speed drives. This work, on one hand, shows the feasibility of spotting broken bars and inter-turn short-circuit by monitoring the motor currents. On the other hand, it shows that the detection of eccentricity and bearing fault by monitoring the motor current is a difficult task.