Detection of inter-turn faults in multi-phase ferrite-PM assisted synchronous reluctance machine

Inter-turn winding faults in five-phase ferrite-permanent magnet-assisted synchronous reluctance motors (fPMa-SynRMs) can lead to catastrophic consequences if not detected in a timely manner, since they can quickly progress into more severe short-circuit faults, such as coil-to-coil, phase-to-ground or phase-to-phase faults. This paper analyzes the feasibility of detecting such harmful faults in their early stage, with only one short-circuited turn, since there is a lack of works related to this topic in multi-phase fPMa-SynRMs. Two methods are tested for this purpose, the analysis of the spectral content of the zero-sequence voltage component (ZSVC) and the analysis of the stator current spectra, also known as motor current signature analysis (MCSA), which is a well-known fault diagnosis method. This paper compares the performance and sensitivity of both methods under different operating conditions. It is proven that inter-turn faults can be detected in the early stage, with the ZSVC providing more sensitivity than the MCSA method. It is also proven that the working conditions have little effect on the sensitivity of both methods. To conclude, this paper proposes two inter-turn fault indicators and the threshold values to detect such faults in the early stage, which are calculated from the spectral information of the ZSVC and the line currentsPeer ReviewedPostprint (published version

Real-Time Fault Diagnosis of Permanent Magnet Synchronous Motor and Drive System

Permanent Magnet Synchronous Motors (PMSMs) have gained massive popularity in industrial applications such as electric vehicles, robotic systems, and offshore industries due to their merits of efficiency, power density, and controllability. PMSMs working in such applications are constantly exposed to electrical, thermal, and mechanical stresses, resulting in different faults such as electrical, mechanical, and magnetic faults. These faults may lead to efficiency reduction, excessive heat, and even catastrophic system breakdown if not diagnosed in time. Therefore, developing methods for real-time condition monitoring and detection of faults at early stages can substantially lower maintenance costs, downtime of the system, and productivity loss. In this dissertation, condition monitoring and detection of the three most common faults in PMSMs and drive systems, namely inter-turn short circuit, demagnetization, and sensor faults are studied. First, modeling and detection of inter-turn short circuit fault is investigated by proposing one FEM-based model, and one analytical model. In these two models, efforts are made to extract either fault indicators or adjustments for being used in combination with more complex detection methods. Subsequently, a systematic fault diagnosis of PMSM and drive system containing multiple faults based on structural analysis is presented. After implementing structural analysis and obtaining the redundant part of the PMSM and drive system, several sequential residuals are designed and implemented based on the fault terms that appear in each of the redundant sets to detect and isolate the studied faults which are applied at different time intervals. Finally, real-time detection of faults in PMSMs and drive systems by using a powerful statistical signal-processing detector such as generalized likelihood ratio test is investigated. By using generalized likelihood ratio test, a threshold was obtained based on choosing the probability of a false alarm and the probability of detection for each detector based on which decision was made to indicate the presence of the studied faults. To improve the detection and recovery delay time, a recursive cumulative GLRT with an adaptive threshold algorithm is implemented. As a result, a more processed fault indicator is achieved by this recursive algorithm that is compared to an arbitrary threshold, and a decision is made in real-time performance. The experimental results show that the statistical detector is able to efficiently detect all the unexpected faults in the presence of unknown noise and without experiencing any false alarm, proving the effectiveness of this diagnostic approach.publishedVersio

Monitoring and damping unbalanced magnetic pull due to eccentricity fault in induction machines: A review

© 2017 IEEE. Condition monitoring can diagnose the inception of fault mechanisms in induction motors, thus avoiding failure and expensive repairs. Therefore, there is a strong need to develop an efficient condition monitoring. The main target is to achieve a relatively low cost and/or non-invasive system which is still powerful in terms of monitoring for online detection of developing faults. The presented paper addresses rotor eccentricity faults and studies conventional monitoring techniques for induction motors. In order to reduce the unbalanced magnetic pull (UMP) in case of an eccentric rotor, the eccentricity-generated additional airgap flux waves should be reduced. The radial forces in an induction motor are calculated, and the characteristics of unbalanced magnetic pull are described

Monitoring and Damping UMP Due Eccentricity Fault in Induction Machines: A Review

© 2016 IEEE. Three-phase induction machines are reliable and widely used in industrial plants. The efficient condition monitoring can diagnose the inception of fault mechanisms in induction motors thus avoiding failure and expensive repairs. Therefore, there is a strong need to develop a more efficient condition monitoring. The main target is to achieve a relatively low cost and/or non-invasive system which is still powerful in terms of monitoring for online detection of developing faults. This digest adresses rotor eccentricity faults and study of conventional monitoring techniques for induction motor faults. In order to reduce the UMP in case of an eccentric rotor, the eccentricity-generated additional airgap flux waves should be reduced. Additional, the characteristics of UMP in induction machines are addressed. Methods to reduce the side-band flux waves and hence attenuate the UMP will be addressed

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

Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection

Inverter-fed induction motors (IMs) contain a serious of current harmonics, which become severer under stator and rotor faults. The resultant fault components in the currents affect the monitoring of the motor status. With this background, the fault components in the electromagnetic torque under stator faults considering harmonics are derived in this paper, and the fault components in current harmonics under rotor faults are analyzed. More importantly, the monitoring based on the fault characteristics (both in the torque and current) is proposed to provide reliable stator and rotor fault diagnosis. Specifically, the fault components induced by stator faults in the electromagnetic torque are discussed in this paper, and then, fault components are characterized in the torque spectrum to identify stator faults. To achieve so, a full-order flux observer is adopted to calculate the torque. On the other hand, under rotor faults, the sidebands caused by time and space harmonics in the current are analyzed and exploited to recognize rotor faults, being the motor current signature analysis (MCSA). Experimental tests are performed on an inverter-fed 2.2 kW/380 V/50 Hz IM, which verifies the analysis and the effectiveness of the proposed fault diagnosis methods of inverter-fed IMs

Failure Detection by signal similarity measurement of Brushless DC motors

During the last years the Brushless DC (BLDC) motors are gaining popularity as a solution for providing mechanical power, starting from low cost mobility solutions like the electric bikes, to high performance and high reliability aeronautical Electro- Mechanical Actuators (EMAs). In this framework, the availability of fault detection tools suited for these types of machines appears necessary. There is already a vast literature on this topic, but only a small percentage of the proposed techniques are developed to a Technology Readiness Level (TRL) sufficiently high to be implementable in industrial applications. The investigation on the state of the art carried out during the first phase of the present work, tries to collect the articles which are closer to a possible implementation. This choice has been influenced by the author experience when dealing with fault detection papers, which often are oriented towards a more academic public and do not concentrate on the implementation. The methodology used in this work to compile the state of the art has been the Systematic Literature Review (SLR) and it is still not diffused in the engineering world. For this reason a dedicated description has been inserted in the respective chapter of the thesis. From this study, some characteristics needed for the fault detection on electric machine have been listed and a new technique for demagnetisation detection on BLDC motors has been proposed. In the second part of the thesis, it is presented an algorithm to detect demagnetisation based on the dissimilarity between the voltages of the various electric turns of the motor due to this failure. The exposed method presents the advantages of not needing domain transforms or previous knowledge of the motor (made exception for the number of pole-pairs). Furthermore the proposed indicators are fast to be computed and require only the acquisition of motor phases voltages for a mechanical turn. The hypotheses made about the effect of a demagnetisation with Finite Element Method (FEM) have also been confirmed through simulations analysis and the proposed method to detect demagnetisation has been validated with experimental tests on a real motor. 2 Applications and Limitations The presented indicators have been studied, simulated and experimented only on an outrunner, low power BLDC motor. Anyway it is not excluded that, with some adaptation, they could be used on any BLDC motor or also on different types of motors; indeed this is an argument for a future work. Another important consideration is that, in order to detect demagnetisation, the motor should have a number of pole pairs greater than 2. This because the algorithm compares the electric turns between them and it is obviously necessary to have more than one. Another characteristic is that it can only detect partial demagnetisation. The demagnetisation of all the magnets to the same level, although very improbable, would not cause those differences in the voltage signals needed for fault detection. Various tests have been executed both at fixed and variable speed. In the first case it was possible to define a threshold to discern between the healthy and the demagnetised motor, while in the second case, even if the indicators are still separated, it was not possible to define a fixed threshold. Hence, if no classification algorithms are used (Support Vector Machine (SVM), Neural Network (NN), Artificial Intelligence (AI), etc.), the indicator shall be computed when the motor is running in steady state conditions. 3 Advantages The method of fault detection by using the proposed indicators has the main advantage of being straightforwardly applicable with no need of extra hardware. Another important characteristic to be highlighted is that the only previous needed knowledge of the motor is the number of pole-pairs. Also the intermediate data are easy to understand as they represent physical variables of the motor in the time domain. Thanks to this, also no domain transformations for frequency analysis are needed, saving computation time. The algorithm to compute the indicators is composed by few steps, it is fast to execute and does not need complex programming or libraries. Indeed the execution time for the PC implementation is already very low and an optimised implementation in a lower level programming language could easily fit in a microcontroller and be executed at even higher speed, permitting both real time monitoring and punctual testing during maintenance. Furthermore it uses only few and easily obtainable data, which makes it suitable for every industrial implementation and interesting for further academic researches. Having a maximum theoretical value for the indicator is also an important advantage, because it permits to evaluate a motor without previous knowledge of the same; indeed a healthy motor should have an ixc value always very close to this maximum value. It is worth to notice that the proposed indicators have been validated with experimental tests in various conditions, showing both good performances and space for further improvements. Finally, although it is true that constant speed is required for a correct analysis, it is needed for just a mechanical turn, i.e. for few milliseconds. For example if the motor is running at 3000 RPM, a complete turn is executed in 20 ms

Rotating Electrical Machines: Types, Applications and Recent Advances

The Rotating Electrical Machines (REMs) are classified into Motors and Generators. They powered the industrial, domestic and commercial loads. Because of their importance. This paper discussed different types of REMs, their applications and recent advances. REMs are applied in Teaching, Domestic, Mechatronics, Motorcycle, Three-wheelers, Electric Vehicle, Healthcare, Flywheel Energy Storage and Wind Energy Conversion Systems. It periscopes the advances of REMs in design, Fault diagnostic, control and condition monitoring. Its significance is to shed light on some advances made in REM

On Innovative Methods of Induction Motor Interturn and Broken-bar Fault Diagnostics

A fault indicator, the so-called swing angle, for broken-bar and interturn faults is investigated in this paper. This fault indicator is based on the rotating magnetic-field pendulous-oscillation concept in faulty squirrel-cage induction motors. Using the swing-angle indicator, it will be demonstrated here that an interturn fault can be detected even in the presence of machine manufacturing imperfections. Meanwhile, a broken-bar fault can be detected under both direct-line and PWM excitations, even under the more difficult condition of partial-load levels. These two conditions of partial load and motor manufacturing imperfections, which are considered as difficult situations for fault detection, are investigated through experimentally obtained test results for a set of 2- and 5-hp induction motors

Studies in Electrical Machines & Wind Turbines associated with developing Reliable Power Generation

The publications listed in date order in this document are offered for the Degree of Doctor of Science in Durham University and have been selected from the author’s full publication list. The papers in this thesis constitute a continuum of original work in fundamental and applied electrical science, spanning 30 years, deployed on real industrial problems, making a significant contribution to conventional and renewable energy power generation. This is the basis of a claim of high distinction, constituting an original and substantial contribution to engineering science