H. Razik

A Novel Monitoring of Load Level and Broken Bar Fault Severity Applied to Squirrel-Cage Induction Motors Using a Genetic Algorithm

This paper deals with the diagnostic of the signature of rotor broken bars when an induction machine is fed or not by an unbalanced line voltage. These signatures are given by the complex spectrum modulus of line current. In order to make the diagnostic, a genetic algorithm is used to keep the amplitude of all faulty lines. Moreover, a fuzzy logic approach allows us to conclude to the load level operating system and to inform the operator of the rotor fault severity. Several experimental results prove the performance of this method under various load levels and various fault severities. Notwithstanding, this approach requires a steady-state operating condition. The conclusion resulting from this paper is highlighted by experimental results which prove the efficiency of the suggested approach.

Estimation of the SOC and the SOH of li-ion batteries, by combining impedance measurements with the fuzzy logic inference

This paper describes experimental results aiming at analyzing lithiumion batteries performances with aging, for different state of charge and temperatures. The results are provided from electrochemical impedance spectroscopy (EIS) measurements on new and aged cells. A climatic test chamber is used during experiments to simulate the conditions of the temperature. Experimental data are plotted over a broad spectrum of frequencies on Nyquist diagrams. We will also inspect the impact of the presence of a DC current during the EIS measurement. Finally, obtained results will be processed in a way to be used in a fuzzy logic system (FLS), to assess either the state of charge (SOC) or the state of health (SOH) of cells.

Detection and compensation of switch faults in a three level inverter

This paper deals with open and short-circuit switch faults in a three-level inverter feeding a three-phsse induction motor. It also investigates the fault compensation via topologies that are able to maintain the system operation for a period of time. Simulated and experimental results verify the feasibility of proposed strategies.

Long-term prediction of bearing condition by the neo-fuzzy neuron

Rolling element bearings are devices used in almost every electrical machine. Therefore, it is important to monitor and track the degradation of bearings. This paper presents a new approach to predict the degradation of bearings by a time series forecasting model called the neo-fuzzy neuron. The proposed approach uses the root mean square extracted from vibration signals as a health indicator. The root mean square is used here as an input of the neo-fuzzy neuron in order to estimate the evolution of bearings degradation in time. Experimental degradation data provided by the University of Cincinnati is used to validate the proposed approach. A comparative study between the neo-fuzzy neuron and the adaptive neuro-fuzzy inference system is carried out to appraise their prediction capabilities. The experimental results show that the neo-fuzzy model can track the degradation of bearings.

An improved model of the induction machine dedicated to faults detection extension of the modified winding function

This paper deals mainly with the modelling of induction machine inductances by taking into account all the space harmonics, the introduction of the skewing rotor bars effects and linear rise of MMF across the slot. The model is established initially in the case of symmetric machine, which corresponds to the case of a constant air-gap. Then in other cases where the machine presents a static or dynamic eccentricity, an axial or radial eccentricity. This objective would be achieved by exploiting an extension in 2-D of the modified winding function approach (MWFA). Moreover, the theoretical aspects are presented and the stator current spectra analysis proves the effectiveness of this approach in case of eccentricity

Accurate modelling of cage induction machine with analytical evaluation of inductances

This paper describes an accurate modelling of squirrel-cage induction machine for the diagnosis purpose. A detailed depiction of the machine permits to determine a general mathematical formula of the self inductances of the stator coils, rotor loops, as well as the mutual inductances between the stator coils and rotor loops. Some high harmonics collected experimentally for both stator line current and the voltage taking place between the supply and the stator neutrals (line neutral voltage) confirm the results obtained by the developed model.

Considerations about the modeling and simulation of air-gap eccentricity in induction motors

This paper proposes, firstly, a precise evaluation of the inductances of induction machine (IM) by transforming the calculations into a simple calculation of surfaces. The skewing bars effect and linear rise of the magneto-motive force (MMF) across the slot in case of constant or nonuniform air-gap are taken into account. Secondly, we consider the two types of star stator winding coupling of IM, YN-connected and Y-connected. The choice of a 2-pole IM in our application is due to the fact that the majority of the previous publications have used a 4-pole IM or more. Also, and particularly, the 2-pole IM can have homopolar flux in presence of eccentricity or odd number of slots per pole. It is the reason for which it deserves a particular interest. The model is established initially in case of symmetric machine, then, in the frequent case of radial eccentricity which is the mixed eccentricity. This objective would be achieved by exploiting an extension in 2-D of the modified winding function approach (MWFA). The analysis is validated by comparing the experimental line-current spectra of the eccentric machine to the simulation results

Independent voltage control for series connected six-phase and three-phase induction machines

A lot of relevant concepts related to series connection and independent control of multiphase machines supplied from a single current-controlled voltage source inverter have been recently demonstrated by some researchers. These previous evaluation give a great contribution to further improvements of multimachine series connected applications. Following the initial proposition, which includes an evaluation based on the general theory of electrical machines, this paper evaluates PWM schemes for two-motor series connected motor drive. The goal of this paper is to show how to achieve independent voltage control and to evaluate the switched voltage profile in the motors windings. Scalar and vector approach are addressed and, simulation and experimental results are shown to illustrate details discussed in the paper.

A methodology to assess the State of Health of lithium-ion batteries based on the battery's parameters and a Fuzzy Logic System

This paper presents a methodology used to assess the State of Health (SOH) of lithium-ion batteries. This methodology can be readily embedded in all battery management devices including the electronics for electric vehicles. The smart estimation method is based on a Fuzzy Logic System (FLS) and two calculated batterys parameters. Therefore it enhances the SOH value based primarily on both the calendar and the cycling life. The FLS is constructed by using only the temperature and the absolute value of the current that the battery sustains. Finally, through the experiments, obtained results for both a new and an aged battery demonstrate the accuracy of the methodology and how it can improve the batterys lifespan.

Detection of induction motor faults by an improved artificial ant clustering

In the last decade, the field of diagnosis has attracted the attention of many researchers, especially for the diagnosis of induction motors. This type of machine is widely used in industry because of its robustness and its specific power. Therefore, the monitoring and diagnosis of these motors become very important. This paper deals with the diagnosis of induction motor faults. The method is based on ant-clustering and it is improved by K-means pattern recognition and Principal Components Analysis (PCA). This approach is applied to the diagnosis of a squirrel-cage induction motor of 5.5kW with broken bars and bearing faults in order to check the detection capability. The obtained results prove the efficiency of this approach.