Robust Diagnosis Method Based on Parameter Estimation for an Interturn Short-Circuit Fault in Multipole PMSM under High-Speed Operation

This paper proposes a diagnosis method for a multipole permanent magnet synchronous motor (PMSM) under an interturn short circuit fault. Previous works in this area have suffered from the uncertainties of the PMSM parameters, which can lead to misdiagnosis. The proposed method estimates the q-axis inductance (L-q) of the faulty PMSM to solve this problem. The proposed method also estimates the faulty phase and the value of G, which serves as an index of the severity of the fault. The q-axis current is used to estimate the faulty phase, the values of G and L-q. For this reason, two open-loop observers and an optimization method based on a particle-swarm are implemented. The q-axis current of a healthy PMSM is estimated by the open-loop observer with the parameters of a healthy PMSM. The L-q estimation significantly compensates for the estimation errors in high-speed operation. The experimental results demonstrate that the proposed method can estimate the faulty phase, G, and L-q besides exhibiting robustness against parameter uncertainties.1165Ysciescopu

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

Advanced Fault Detection Methods for Permanent Magnets Synchronous Machines

The trend in recent years of transport electrification has significantly increased the demand for reliability and availability of electric drives, particularly in those employing Permanent Magnet Synchronous Machines (PMSM), often selected due to their high efficiency and energy density. Fault detection has been identified as one of the key aspects to cover such demand. Stator winding faults are known to be the second most common type of fault, after bearing fault. An extensive literature review has shown that, although a number of methods has been proposed to address this type of fault, no tool of general application, capable of dealing effectively with fault detection under transient conditions unrelated to the fault, has been proposed up to date. This thesis has made contributions to modelling, real-time emulation and stator winding fault detection of PMSM. Fault detection has been carried out through model-based and signal-based methods with a specific aim at operation during transient conditions. Furthermore, fault classification methods already available have been implemented with features computed by proposed signal-based fault detection methods. The main conclusion drawn from this thesis is that model-based fault detection methods, particularly those based on residuals, appear to be better suited for transient conditions analysis, as opposed to signal-based fault detection methods. However, it is expected that a combination of the two (model/signal) would yield the best results

Real-Time Detection of Incipient Inter-Turn Short Circuit and Sensor Faults in Permanent Magnet Synchronous Motor Drives Based on Generalized Likelihood Ratio Test and Structural Analysis

This paper presents a robust model-based technique to detect multiple faults in permanent magnet synchronous motors (PMSMs), namely inter-turn short circuit (ITSC) and encoder faults. The proposed model is based on a structural analysis, which uses the dynamic mathematical model of a PMSM in an abc frame to evaluate the system’s structural model in matrix form. The just-determined and over-determined parts of the system are separated by a Dulmage–Mendelsohn decomposition tool. Subsequently, the analytical redundant relations obtained using the over-determined part of the system are used to form smaller redundant testable sub-models based on the number of defined fault terms. Furthermore, four structured residuals are designed based on the acquired redundant sub-models to detect measurement faults in the encoder and ITSC faults, which are applied in different levels of each phase winding. The effectiveness of the proposed detection method is validated by an in-house test setup of an inverter-fed PMSM, where ITSC and encoder faults are applied to the system in different time intervals using controllable relays. Finally, a statistical detector, namely a generalized likelihood ratio test algorithm, is implemented in the decision-making diagnostic system resulting in the ability to detect ITSC faults as small as one single short-circuited turn out of 102, i.e., when less than 1% of the PMSM phase winding is short-circuited.publishedVersio

Non-Invasive Real-Time Diagnosis of PMSM Faults Implemented in Motor Control Software for Mission Critical Applications

This paper presents a non-intrusive, real-time, online Condition Monitoring and FaultDiagnosis system for Permanent Magnet Synchronous Machines. The system utilizesonly the motor drive's built-in sensors, such as current and voltage sensors, to detectthree types of faults: inter-turn short circuit, partial demagnetization, and staticeccentricity. The proposed solution adopts a hardware-free approach, utilizingcurrent/voltage signature analysis to optimize cost-effectiveness. It requires a smallmemory and short execution time, allowing it to be implemented on a simple motorcontroller with limited memory and calculation power. The system is designed forcritical mission applications, and therefore, computation load, code size, memoryallocation, and run-time optimization are key focuses for real-time operation. Theproposed method has a high detection accuracy of 98%, is computationally efficient,and can accurately detect and classify the fault. The system provides immediateinsights into motor health without interrupting the drive operation

Modern Diagnostics Techniques for Electrical Machines, Power Electronics, and Drives

© 2015 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] For the last ten years, at least three different special sections dealing with diagnostics in power electrical engineering have been published in the IEEE transactions on industrial electronics [1]-[5]. All of them had their specificities, but the last ones, starting in 2011, were more connected to relevant events organized on the topic. In fact, these events have been clearly the only international forums fully dedicated to diagnostics techniques in power electrical engineering. For this particular issue, it has been decided to separate the different submissions into six parts: state of the art; general methods; induction machines (IMs); synchronous machines (SMs); . electrical drives; power components and power converters. The second section includes only one state-of-the-art paper, which is dedicated to actual techniques implemented in both industry and research laboratories. The third section includes three papers on diagnostic techniques not specifically aimed at a particular type of machine. The fourth section includes three papers devoted to diagnostics of rotor faults, two dedicated to stator insulation issues, and four papers dealing with mechanical faults diagnosis in IMs. The fifth section includes papers focusing on different types of SMs. The first two papers deal with wound-rotor SMs, the following three papers are dedicated to permanent-magnet radial flux machines, and the last one deals with permanent-magnet axial flux machines. Regarding the types of faults analyzed, there are three papers devoted to the diagnosis of interturn short circuits in the stator windings, i.e., one dedicated to the detection and location of field-winding-to-ground faults and a paper devoted to the diagnosis of static eccentricities. In the sixth section, two papers investigate issues related to faults in drive sensors, and one is devoted to fault detections in the coupling inductors. The last section includes two papers devoted to diagnosis of faults and losses analysis in switching components of power converters.Capolino, G.; Antonino-Daviu, J.; Riera-Guasp, M. (2015). Modern Diagnostics Techniques for Electrical Machines, Power Electronics, and Drives. IEEE Transactions on Industrial Electronics. 62(3):1738-1745. doi:10.1109/TIE.2015.2391186S1738174562

Mixed Fault Classification of Sensorless PMSM Drive in Dynamic Operations Based on External Stray Flux Sensors

This paper aims to classify local demagnetisation and inter-turn short-circuit (ITSC) on position sensorless permanent magnet synchronous motors (PMSM) in transient states based on external stray flux and learning classifier. Within the framework, four supervised machine learning tools were tested: ensemble decision tree (EDT), k-nearest neighbours (KNN), support vector machine (SVM), and feedforward neural network (FNN). All algorithms are trained on datasets from one operational profile but tested on other different operation profiles. Their input features or spectrograms are computed from resampled time-series data based on the estimated position of the rotor from one stray flux sensor through an optimisation problem. This eliminates the need for the position sensors, allowing for the fault classification of sensorless PMSM drives using only two external stray flux sensors alone. Both SVM and FNN algorithms could identify a single fault of the magnet defect with an accuracy higher than 95% in transient states. For mixed faults, the FNN-based algorithm could identify ITSC in parallel-strands stator winding and local partial demagnetisation with an accuracy of 87.1%.publishedVersio

Stator inter-turn faults diagnosis in induction motors using zero-sequence signal injection

This study presents a strategy for stator inter-turn faults diagnosis in induction motors (IMs) operating under timevariable load and time-variable speed conditions. The strategy consists in injecting a zero-sequence high-frequency signal in order to analyse variations in the stator inductances. Incipient stator inter-turn faults are detected by a simple signal processing of the derivatives of the currents. A feature of the strategy is that the zero-sequence high-frequency signal is generated by the inverter that feeds the machine, without modifying the standard space vector modulation of the IM-drive. Experimental results show that faults representing <1% of the stator winding can be detected, as well as the phase location of the fault, validating this proposal.Fil: Otero, Marcial. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados; ArgentinaFil: de la Barrera, Pablo Martin. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados; ArgentinaFil: Bossio, Guillermo Rubén. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados; ArgentinaFil: Leidhold, Roberto. Otto-von-Guericke-Universität Magdeburg; Alemani

Modelling and Detecting Faults of Permanent Magnet Synchronous Motors in Dynamic Operations

Paper VI is excluded from the dissertation until the article will be published.Permanent magnet synchronous motors (PMSMs) have played a key role in commercial and industrial applications, i.e. electric vehicles and wind turbines. They are popular due to their high efficiency, control simplification and large torque-to-size ratio although they are expensive. A fault will eventually occur in an operating PMSM, either by improper maintenance or wear from thermal and mechanical stresses. The most frequent PMSM faults are bearing faults, short-circuit and eccentricity. PMSM may also suffer from demagnetisation, which is unique in permanent magnet machines. Condition monitoring or fault diagnosis schemes are necessary for detecting and identifying these faults early in their incipient state, e.g. partial demagnetisation and inter-turn short circuit. Successful fault classification will ensure safe operations, speed up the maintenance process and decrease unexpected downtime and cost. The research in recent years is drawn towards fault analysis under dynamic operating conditions, i.e. variable load and speed. Most of these techniques have focused on the use of voltage, current and torque, while magnetic flux density in the air-gap or the proximity of the motor has not yet been fully capitalised. This dissertation focuses on two main research topics in modelling and diagnosis of faulty PMSM in dynamic operations. The first problem is to decrease the computational burden of modelling and analysis techniques. The first contributions are new and faster methods for computing the permeance network model and quadratic time-frequency distributions. Reducing their computational burden makes them more attractive in analysis or fault diagnosis. The second contribution is to expand the model description of a simpler model. This can be achieved through a field reconstruction model with a magnet library and a description of both magnet defects and inter-turn short circuits. The second research topic is to simplify the installation and complexity of fault diagnosis schemes in PMSM. The aim is to reduce required sensors of fault diagnosis schemes, regardless of operation profiles. Conventional methods often rely on either steady-state or predefined operation profiles, e.g. start-up. A fault diagnosis scheme robust to any speed changes is desirable since a fault can be detected regardless of operations. The final contribution is the implementation of reinforcement learning in an active learning scheme to address the imbalance dataset problem. Samples from a faulty PMSM are often initially unavailable and expensive to acquire. Reinforcement learning with a weighted reward function might balance the dataset to enhance the trained fault classifier’s performance.publishedVersio