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

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

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 Reviewe