37 research outputs found
Electrical and magnetic faults diagnosis in permanent magnet synchronous motors
Permanent magnet synchronous motors (PMSMs) are an alternative in critical applications where high-speed operation,
compactness and high efficiency are required. In these applications it is highly desired to dispose of an on-line, reliable and
cost-effective fault diagnosis method. Fault prediction and diagnosis allows increasing electric machines performance and
raising their lifespan, thus reducing maintenance costs, while ensuring optimum reliability, safe operation and timely
maintenance. Consequently this thesis is dedicated to the diagnosis of magnetic and electrical faults in PMSMs.
As a first step, the behavior of a healthy machine is studied, and with this aim a new 2D finite element method (FEM) modelbased
system for analyzing surface-mounted PSMSs with skewed rotor magnets is proposed. It is based on generating a
geometric equivalent non-skewed permanent magnet distribution which accounts for the skewed distribution of the practical
rotor, thus avoiding 3D geometries and greatly reducing the computational burden of the problem.
To diagnose demagnetization faults, this thesis proposes an on-line methodology based on monitoring the zero-sequence
voltage component (ZSVC). Attributes of the proposed method include simplicity, very low computational burden and high
sensibility when compared with the well known stator currents analysis method. A simple expression of the ZSVC is
deduced, which can be used as a fault indicator parameter. Furthermore, mechanical effects arising from demagnetization
faults are studied. These effects are analyzed by means of FEM simulations and experimental tests based on direct
measurements of the shaft trajectory through self-mixing interferometry. For that purpose two perpendicular laser diodes are
used to measure displacements in both X and Y axes. Laser measurements proved that demagnetization faults may induce
a quantifiable deviation of the rotor trajectory.
In the case of electrical faults, this thesis studies the effects of resistive unbalance and stator winding inter-turn short-circuits
in PMSMs and compares two methods for detecting and discriminating both faults. These methods are based on monitoring
and analyzing the third harmonic component of the stator currents and the first harmonic of the ZSVC.
Finally, the Vold-Kalman filtering order tracking algorithm is introduced and applied to extract selected harmonics related to
magnetic and electrical faults when the machine operates under variable speed and different load levels. Furthermore,
different fault indicators are proposed and their behavior is validated by means of experimental data. Both simulation and
experimental results show the potential of the proposed methods to provide helpful and reliable data to carry out a
simultaneous diagnosis of resistive unbalance and stator winding inter-turn faults
Fault detection in dynamic conditions by means of discrete wavelet decomposition for PMSM running under bearing damage
This paper presents a study of the Permanent
Magnet Synchronous Machine (PMSM) running under bearing damage. To carry out the study a two-dimensional (2-D) Finite Element Analysis (FEA) is used. Stator current induced harmonics for fault condition were investigated. Advanced signal analysis by means of Continuous and Discrete Wavelet
Transforms was performed. Simulation were carried out and compared with experimental.Peer ReviewedPostprint (published version
Detection of inter-turns short circuits in permanent magnet synchronous motors operating under transient conditions by means of the zero sequence voltage
This work proposes the zero sequence voltage component (ZSVC) of the stator three-phase voltages as a method for detecting winding inter-turns short circuits in permanent magnet synchronous motors PMSM operating under transient conditions. Additionally it proves the linear relationship between the
ZSVC and speed, which is effectively used as a fault severity index. The acquired ZSVC temporal signal is processed by means of the Hilbert-Huang transform (HHT).
Experimental results presented in this work show the advantages of the method to provide helpful data for online diagnosis of stator winding inter-turn faults.Peer ReviewedPostprint (author’s final draft
A multi-objective GA to demand-side management in an automated warehouse
The simultaneous operation of the automated storage and retrieval machines (ASRs) in an automated warehouse can increase the likelihood that high power demand peaks turn unstable the electric system.
Furthermore, high power peaks mean the need for more electrical power contracted, which in turns leads to more fixed operation cost and inefficient use of the electrical installations. In this context, we present a
multi-objective genetic algorithm approach (MOGA) to implement demand-side management (DSM) in an automated warehouse. It works minimizing the total energy demand, but without increasing substantially the time for the operation. Simulations show the
performances of the new approach.Peer ReviewedPostprint (published version
Condition monitoring system for characterization of electric motor ball bearings with distributed fault using fuzzy inference tools
The present work shows a condition monitoring
system applied to electric motors ball bearings. Unlike most of the previous work on this area, which is mainly focused on the
location of single-point defects in bearing components – inner and outer races, cage or ball faults -, this research covers wide
range irregularities which are very often more difficult to analyse. In addition to traditional techniques like vibration and
current signals, high frequency current bearing pulses and acoustic emissions are also analysed. High frequency bearings
current pulses are acquired using motors especially modified. This modification isolates ball bearings from the motor stator
frame, except for a bearing housing single point connected to ground through a proper cable where the pulses signal is measured. A multivariable fuzzy inference analysis approach is presented to get around the diagnosis difficulty.Peer ReviewedPostprint (published version
Evaluation of machine learning techniques for electro-mechanical system diagnosis
The application of intelligent algorithms, in electro-mechanical diagnosis systems, is increasing in order to reach high Reliability and performance ratios in critical and complex scenarios. In this context, different multidimensional intelligent diagnosis systems, based on different machine learning techniques, are presented and evaluated in an electro-mechanical actuator diagnosis scheme. The used diagnosis methodology includes the acquisition of different physical magnitudes from the system, such as machine vibrations and stator currents, to enhance the monitoring capabilities. The features
calculation process is based on statistical time and frequency domains features, as well as timefrequency fault indicators. A features reduction stage is, additionally, included to compress the descriptive fault information in a reduced feature set. After, different classification algorithms such as
Support Vector Machines, Neural Network, k-Nearest Neighbors and Classification Trees are implemented. Classification ratios over inputs corresponding to previously learnt classes, and generalization capabilities with inputs corresponding to learnt classes slightly modified are evaluated in an experimental test bench to analyze the suitability of each algorithm for this kind of application.Peer ReviewedPostprint (author’s final draft
Mixed resistive unbalance and winding inter-turn faults model of permanent magnet synchronous motors
La publicación final está disponible en Springer a través de 10.1007/s00202-014-0316-zThis work analyzes the behavior of surface-mounted permanent magnet synchronous motors (SPMSMs) operating under stator faults. The studied faults are resistive unbalance and winding inter-turn short circuits, which may lead to unbalanced conditions of the motor. Both faults may reduce motor efficiency and performance and produce premature ageing. This work develops an analytical model of the motor when operating under stator faults. By this way, the theoretical basis to understand the effects of resistive unbalance and stator winding inter-turn faults in SPMSMs is settied. This work also compares two methods for detecting and discriminating both faults. For this purpose, a method based on the analysis of the zero-sequence voltage component is presented, which is compared to the traditional method, i.e. the analysis of the stator currents harmonics. Both simulation and experimental results presented in this work show the potential of the zero-sequence voltage component method to provide helpful and reliable data to carry out a simultaneous diagnosis of resistive unbalance and stator winding inter-turn faults.Peer ReviewedPostprint (author's final draft
Application of the zero-sequence voltage component to detect stator winding inter-turn faults in PMSMs
This paper develops and analyzes a methodology for detecting stator winding inter-turn faults in surface-mounted permanent magnet synchronous motors. The proposed methodology is based on monitoring the zero-sequence voltage component having into account the effects of the inverter that usually feeds the machine. The theoretical basis of such a method is established from the parametric model of the machine. Attributes of the method presented here include simplicity, high accuracy, low computational burden and high sensibility. Additionally, it is especially useful when dealing with fault tolerant systems. From this model the expression of the zero sequence voltage component is deduced, which is used to detect stator winding inter-turn faults. Both simulation and experimental results presented in this work show the potential of the proposed method to provide helpful and reliable data to carry out an online diagnosis of such faults.Peer ReviewedPostprint (published version
Application of the zero-sequence voltage component to detect stator winding inter-turn faults in PMSMs
This paper develops and analyzes a methodology for detecting stator winding inter-turn faults in surface-mounted permanent magnet synchronous motors. The proposed methodology is based on monitoring the zero-sequence voltage component having into account the effects of the inverter that usually feeds the machine. The theoretical basis of such a method is established from the parametric model of the machine. Attributes of the method presented here include simplicity, high accuracy, low computational burden and high sensibility. Additionally, it is especially useful when dealing with fault tolerant systems. From this model the expression of the zero sequence voltage component is deduced, which is used to detect stator winding inter-turn faults. Both simulation and experimental results presented in this work show the potential of the proposed method to provide helpful and reliable data to carry out an online diagnosis of such faults.Peer Reviewe
Detection of interturn faults in PMSMs with different winding configuration
Interturn faults in permanent magnet synchronous motors (PMSMs) may develop fast into more severe faults such as coil-to-coil, phase-to-phase and phase-to-ground short circuits. These faults are very destructive and may irreversibly damage the PMSM. Therefore, it is highly desirable to develop suitable methods for the early detection of such faults. The effects of interturn faults are visible in both the stator currents and the zero-sequence voltage component (ZSVC) spectra. By designing appropriate fault diagnosis schemes based on the analysis of the harmonic content of such electric variables it is possible to detect short circuit faults in its early stage. However, the stator winding configuration of the PMSM deeply impacts the harmonic content of both spectra. This paper studies the effects of different stator winding configurations in both the stator currents and the ZSVC spectra of healthy and faulty machines. Results presented may help to develop fault diagnosis schemes based on the acquisition and further analysis of the stator currents and/or the ZSVC harmonic components.Postprint (published version