Dahaman Ishak

Fault Detection and Diagnosis of Induction Motors Using Motor Current Signature Analysis and a Hybrid FMM–CART Model

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

Offline and online fault detection and diagnosis of induction motors using a hybrid soft computing model

In this paper, a hybrid soft computing model comprising the Fuzzy Min-Max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis is described. Specifically, the hybrid model, known as FMM-CART, is used to detect and classify fault conditions of induction motors in both offline and online environments. A series of experiments is conducted, whereby the Motor Current Signature Analysis (MCSA) method is applied to form a database containing stator current signatures under different motor conditions. The signal harmonics from the power spectral density (PSD) are extracted, and used as the discriminative input features for fault classification with FMM-CART. Three main induction motor conditions, viz. broken rotor bars, stator winding faults, and unbalanced supply, are used to evaluate the effectiveness of FMM-CART. The results indicate that FMM-CART is able to detect motor faults in the early stage, in order to avoid further damage to the induction motor as well as the overall machine or system that uses the motor in its operations.

PWM SWITCHING STRATEGY FOR TORQUE RIPPLE MINIMIZATION IN BLDC MOTOR

PWM Switching Strategy for Torque Ripple Minimization in BLDC Motor This paper describes a new PWM switching strategy to minimize the torque ripples in BLDC motor which is based on sensored rotor position control. The scheme has been implemented using a PIC microcontroller to generate a modified Pulse Width Modulation (PWM) signals for driving power inverter bridge. The modified PWM signals are successfully applied to the next up-coming phase current such that its current rise is slightly delayed during the commutation instant. Experimental results show that the current waveforms of the modified PWM are smoother than that in conventional PWM technique. Hence, the output torque exhibits lower ripple contents.

Anew scheme sensorless control of BLDC motor using software PLL and third harmonic back-emf

High performance sensorless permanent magnet brushless DC motor operation requires proper and precise commutation sequence. In general, the average torque falls rapidly as the rotor speed increases above the base speed, hence the motor will frequently be found to have insufficient torque at higher speed. One way to counter this problem is by using third harmonic back-emf detection technique during high speed operation because the third harmonic is usually kept a constant relationship with rotor position for any motor speeds and load conditions. Therefore, we could potentially avoid the torque deterioration which commonly occurs in traditional sensorless technique due to the delay of the phase angle between current and back-emf. Furthermore this method is practically free of noise which may be introduced from inverter switches. In this work, we propose a new software scheme of phase-locked loop (PLL) of third harmonic back-emf detection in order to accomplish a precise switching strategy for improving torque produced during high speed operation.

A comparative study of PI, fuzzy and hybrid PI-Fuzzy controller for speed control of brushless dc motor drive

This paper presents the comparative study between PI, fuzzy and hybrid PI-Fuzzy controller for speed control of brushless dc (BLDC) motor. The control structure of the proposed drive system is described. The simulation results of the drive system for different operation modes are evaluated and compared. A fuzzy controller offers better speed response for start-up while PI controller has good compliance over variation of load torque but has slow settling response. Hybrid controller has an advantage of integrating a superiority of these two controllers for better control performances. Matlab/Simulink is used to carry out the simulation.

A Comprehensive Analytical Subdomain Model and Its Field Solutions for Surface-Mounted Permanent Magnet Machines

This paper presents a comprehensive analytical subdomain model together with its field solutions for predicting the magnetic field distributions in surface-mounted permanent magnet (PM) machines. The tooth tips and slotting effects during open-circuit, armature reaction, and on-load conditions are considered when deriving the model and developing its solutions. The model derivations and field solutions are extended from a previous model, and can be applied to PM machines with any combinations of slot and pole numbers and any magnetization patterns in the magnets. This model is initially formulated according to Laplaces and Poissons equations in 2-D polar coordinates by the separation of variables technique in four subdomains, such as magnet, airgap, winding slots, and slot-openings. The field solution of each subdomain is obtained applying the appropriate boundary conditions and interface conditions between every two subdomains, respectively, which can precisely account for the mutual influence between slots. Finite element analysis (FEA) is later deployed to validate the analytical results in a surface-mounted PM machine that has nonoverlapping winding arrangement. For validation purposes, PM machines having 3-slot/2-pole with parallel magnetization and 12-slot/10-pole with either parallel or radial magnetizations are used for comparisons. Computation of global quantities for the motor which include the phase back-EMF and cogging torque is also included. The results indicate that the proposed analytical model can accurately predict the magnetic field distributions in each subdomain and the motors global quantities, which are in good agreement with those obtained from the FEA.

Application of the fuzzy min–max neural network to fault detection and diagnosis of induction motors

In this paper, an application of the motor current signature analysis (MCSA) method and the fuzzy min–max (FMM) neural network to detection and classification of induction motor faults is described. The finite element method is employed to generate simulated data pertaining to changes in the stator current signatures under different motor conditions. The MCSA method is then used to process the stator current signatures. Specifically, the power spectral density is employed to extract harmonics features for fault detection and classification with the FMM network. Various types of induction motor faults, which include stator winding faults and eccentricity problems, under different load conditions are experimented. The results are analyzed and compared with those from other methods. The outcomes indicate that the proposed technique is effective for fault detection and diagnosis of induction motors under different conditions.

A speed sensorless field oriented control of parallel- connected dual PMSM

This paper presents a speed sensorless field oriented control of parallel-connected dual permanent magnet synchronous motors (PMSM) fed by a single inverter. The speed of the both motors is controlled by using FOC method and averaging technique. In this project, model reference adaptive system (MRAS) method has been applied to the system in order to eliminate the use of speed sensor. The simulation work is implemented in Matlab/Simulink software in order to verify the proposed control strategy. The effectiveness of this proposed control strategy was confirmed during motor operations with balanced and unbalanced load levels at different speed range.

Study on the electromagnetic force affected by short-circuit current in vertical and horizontal arrangement of busbar system

Each busbar conductor of a phase is subjected to a force due to the short-circuit currents. In this paper, the electromagnetic forces affected by the short-circuit current in three-phase busbar conductor are calculated in vertical and horizontal arrangement. The short-circuit current densities are calculated mathematically. The calculations are performed by assuming a peak value of steady-state ac current is equal to the peak value of the short-circuit current. The electromagnetic forces due to the short-circuit current are calculated according to the equation introduced by IEC Standards 865/1993. The electromagnetic force generated in vertical arrangement is compared with the horizontal of busbar. The result depicted that the busbar in vertical arrangement has about 2 times higher electromagnetic force compared with that in horizontal arrangement. The arrangement of the busbar obviously influences the strength of electromagnetic force due to short-circuit current. Furthermore, the electromagnetic force obtained from the simulation by finite element method in vertical arrangement was agree with the calculation obtained using IEC Standard 865/1993.

Analytical modeling of permanent magnet excited brushed DC motor for low-cost applications

In this paper, we present an analytical modelling of permanent magnet excited brushed DC motor for cost sensitive applications. General solutions to the Laplacian/quasi-Poissonian magnetic field equations are first derived in the motor airgap. Applying the specified boundary conditions, normal and tangential components of flux density in the airgap can be analytically predicted. With distributed windings employed in the armature slots, their flux linkage, induced voltage and motor performance can also be calculated. The proposed analytical model can also be used to parameterize the brushed DC motor for optimal performance such as minimum torque ripple. The proposed model shows very good agreement with the results obtained from FEM. Furthermore, the analytical model compares the calculated results between the 12- slot armature DC motor and 10 -slot armature DC motor.