Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

© 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] Recently, research concerning electrical machines and drives condition monitoring and fault diagnosis has experienced extraordinarily dynamic activity. The increasing importance of these energy conversion devices and their widespread use in uncountable applications have motivated significant research efforts. This paper presents an analysis of the state of the art in this field. The analyzed contributions were published in most relevant journals and magazines or presented in either specific conferences in the area or more broadly scoped events.Riera-Guasp, M.; Antonino-Daviu, J.; Capolino, G. (2015). Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art. IEEE Transactions on Industrial Electronics. 62(3):1746-1759. doi:10.1109/TIE.2014.2375853S1746175962

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

Rotor-Bar Breakage Mechanism and Prognosis in an Induction Motor

[EN] This paper proposes a condition-based maintenance and prognostics and health management (CBM/ PHM) procedure for a rotor bar in an induction motor. The methodology is based on the results of a fatigue test intended to reproduce in the most natural way a bar breakage in order to carry out a comparison between transient and stationary diagnosis methods for incipient fault detection. Newly developed techniques in stator-current transient analysis have allowed tracking the developing fault during the last part of the test, identifying the failure mechanism, and establishing a physical model of the process. This nonlinear failure model is integrated in a particle filtering algorithm to diagnose the defect at an early stage and predict the remaining useful life of the bar. An initial generalization of the results to conditions differing from the ones under which the fatigue test was developed is studied.Climente Alarcon, V.; Antonino-Daviu, J.; Strangas, EG.; Riera-Guasp, M. (2015). Rotor-Bar Breakage Mechanism and Prognosis in an Induction Motor. IEEE Transactions on Industrial Electronics. 62(3):1814-1825. doi:10.1109/TIE.2014.2336604S1814182562

A critical comparison between DWT and Hilbert-Huang-based methods for the diagnosis of rotor bar failures in induction machines

(c) 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising 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] In this paper, a cutting-edge time-frequency decomposition tool, i.e., the Hilbert-Huang transform (HHT), is applied to the stator startup current to diagnose the presence of rotor asymmetries in induction machines. The objective is to extract the evolution during the startup transient of the left sideband harmonic (LSH) caused by the asymmetry, which constitutes a reliable evidence of the presence of the fault. The validity of the diagnosis methodology is assessed through several tests developed using real experimental signals. Moreover, in this paper, an analytical comparison with an alternative time-frequency decomposition tool, i.e., the discrete wavelet transform (DWT), is carried out. This tool was applied in previous works to the transient extraction of fault-related components, with satisfactory results, even in cases in which the classical Fourier approach does not lead to correct results. The results of the application of the HHT and DWT are analyzed and compared, obtaining novel conclusions about their respective suitability for the transient extraction of asymmetry-related components, as well as the equivalence, with regard to the LSH extraction, between their basic components, namely: 1) intrinsic mode function, for the HHT, and 2) approximation signal for the DWT.This work was supported in part by the Spanish “Ministerio de Educación y Ciencia,” in the framework of the “Programa Nacional de proyectos de Investigación Fundamental,” project reference DPI2008-06583/DPI and in part by “Vicerrectorado de Investigación, Desarrollo e Innovación of the Universidad Politécnica de Valencia” through the Programa de Apoyo a la Investigación y Desarrollo under Contract PAID-06-07.Antonino-Daviu, J.; Riera-Guasp, M.; Pineda-Sanchez, M.; Pérez, RB. (2009). A critical comparison between DWT and Hilbert-Huang-based methods for the diagnosis of rotor bar failures in induction machines. IEEE Transactions on Industry Applications. 45(5):1794-1803. https://doi.org/10.1109/TIA.2009.2027558S1794180345

Validation of a New Method for the Diagnosis of Rotor bar Failures via Wavelet Transformation in Industrial Induction Machines

[EN] In this paper, the authors propose a method for the diagnosis of rotor bar failures in induction machines, based on the analysis of the stator current during the startup using the discrete wavelet transform (DWT). Unlike other approaches, the study of the high-order wavelet signals resulting from the decomposition is the core of the proposed method. After an introduction of the physical and mathematical bases of the method, a description of the proposed approach is given; for this purpose, a numerical model of induction machine is used in such a way that the effects of a bar breakage can clearly be shown, avoiding the influence of other phenomena not related with the fault. Afterward, the new diagnosis method is validated using a set of commercial induction motors. Several experiments are developed under different machine conditions (healthy machine and machine with different levels of failure) and operating conditions (no load, full load, pulsating load, and fluctuating voltage). In each case, the results are compared with those obtained using the classical approach, based on the analysis of the steady-state current using the Fourier transform. Finally, the results are discussed, and some considerations about the influence of the DWT parameters (type of mother wavelet, order of the mother wavelet, sampling rate, or number of levels of the decomposition) over the diagnosis are done.Antonino-Daviu, J.; Riera-Guasp, M.; Roger-Folch, J.; Molina Palomares, MP. (2006). Validation of a New Method for the Diagnosis of Rotor bar Failures via Wavelet Transformation in Industrial Induction Machines. IEEE Transactions on Industry Applications. 42(4):990-996. doi:10.1109/TIA.2006.876082S99099642

The Use of the Wavelet Approximation Signal as a Tool for the Diagnosis of Rotor Bar Failures

(c) 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising 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] The aim of this paper is to present a new approach for rotor bar failure diagnosis in induction machines. The method focuses on the study of an approximation signal resulting from the wavelet decomposition of the startup stator current. The presence of the left sideband harmonic is used as evidence of the rotor failure in most diagnosis methods based on the analysis of the stator current. Thus, a detailed description of the evolution of the left sideband harmonic during the startup transient is given in this paper; for this purpose, a method for calculating the evolution of the left sideband during the startup is developed, and its results are physically explained. This paper also shows that the approximation signal of a particular level, which is obtained from the discrete wavelet transform of the startup stator current, practically reproduces the time evolution of the left sideband harmonic during the startup. The diagnosis method proposed here consists of checking if the selected approximation signal fits well the characteristic shape of the left sideband harmonic evolution described in this paper. The method is validated through laboratory tests. The results prove that it can constitute a useful tool for the diagnosis of rotor bar breakages.Riera-Guasp, M.; Antonino-Daviu, J.; Roger-Folch, J.; Molina Palomares, MP. (2008). The Use of the Wavelet Approximation Signal as a Tool for the Diagnosis of Rotor Bar Failures. IEEE Transactions on Industry Applications. 44(3):716-726. doi:10.1109/TIA.2008.921432S71672644

Application and Optimization of the Discrete Wavelet Transform for the Detection of Broken Rotor Bars in Induction Machines

[EN] The problem of the bar breakage diagnosis in electrical induction cage machines is a matter of increasing concern nowadays, due to the widely spread use of these machines in the industry. The classical approach, focused on the Fourier analysis of the steady-state current, has some drawbacks that could be avoided if a study of the transient behavior of the machine is performed. The discrete wavelet transform (DWT) is an ideal tool for this purpose, due to its suitability for the analysis of signals whose frequency spectrum is variable in time. The paper shows how the study of the high-level signals resulting from the DWT of the transient starting current of an induction motor allows the detection of a particular characteristic harmonic that occurs when a rotor bar breakage has taken place. This constitutes an alternative approach that avoids some problems that the traditional method implies and that can even lead to a wrong diagnosis of the fault. In the work, the application of the DWT for broken bar detection is optimized, regarding certain parameters of the transform such as type of the mother wavelet, number of decomposition levels, order of the mother wavelet and sampling frequency. (C) 2006 Elsevier Inc. All rights reserved.Antonino-Daviu, J.; Riera-Guasp, M.; Roger-Folch, J.; Martínez Jiménez, F.; Peris Manguillot, A. (2006). Application and Optimization of the Discrete Wavelet Transform for the Detection of Broken Rotor Bars in Induction Machines. Applied and Computational Harmonic Analysis. 21(2):268-279. doi:10.1016/j.acha.2005.12.003S26827921

The Harmonic Order Tracking Analysis Method for the Fault Diagnosis in Induction Motors Under Time-Varying Conditions

[EN] This paper introduces a new approach for improving the fault diagnosis in induction motors under time-varying conditions. A significant amount of published approaches in this field rely on representing the stator current in the time-frequency domain, and identifying the characteristic signatures that each type of fault generates in this domain. However, time-frequency transforms produce three-dimensional (3-D) representations, very costly in terms of storage and processing resources. Moreover, the identification and evaluation of the fault components in the time-frequency plane requires a skilled staff or advanced pattern detection algorithms. The proposed methodology solves these problem by transforming the complex 3-D spectrograms supplied by time-frequency tools into simple x-y graphs, similar to conventional Fourier spectra. These graphs display a unique pattern for each type of fault, even under supply or load time-varying conditions, making easy and reliable the diagnostic decision even for nonskilled staff. Moreover, the resulting patterns can be condensed in a very small dataset, reducing greatly the storage or transmission requirements regarding to conventional spectrograms. The proposed method is an extension to nonstationary conditions of the harmonic order tracking approach. It is introduced theoretically and validated experimentally by using the commercial induction motors feed through electronic converters.This work was supported by the Spanish "Ministerio de Economia y Competitividad" in the framework of the "Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad" (Project reference DPI2014-60881-R). Paper no. TEC-00176-2016.Sapena-Bano, A.; Burriel-Valencia, J.; Pineda-Sanchez, M.; Puche-Panadero, R.; Riera-Guasp, M. (2017). The Harmonic Order Tracking Analysis Method for the Fault Diagnosis in Induction Motors Under Time-Varying Conditions. IEEE Transactions on Energy Conversion. 32(1):244-256. doi:10.1109/TEC.2016.2626008S24425632

Fault Diagnosis in the Slip Frequency Plane of Induction Machines Working in Time-Varying Conditions

[EN] Motor current signature analysis (MCSA) is a fault diagnosis method for induction machines (IMs) that has attracted wide industrial interest in recent years. It is based on the detection of the characteristic fault signatures that arise in the current spectrum of a faulty induction machine. Unfortunately, the MCSA method in its basic formulation can only be applied in steady state functioning. Nevertheless, every day increases the importance of inductions machines in applications such as wind generation, electric vehicles, or automated processes in which the machine works most of time under transient conditions. For these cases, new diagnostic methodologies have been proposed, based on the use of advanced time-frequency transforms-as, for example, the continuous wavelet transform, the Wigner Ville distribution, or the analytic function based on the Hilbert transform-which enables to track the fault components evolution along time. All these transforms have high computational costs and, furthermore, generate as results complex spectrograms, which require to be interpreted for qualified technical staff. This paper introduces a new methodology for the diagnosis of faults of IM working in transient conditions, which, unlike the methods developed up to today, analyzes the current signal in the slip-instantaneous frequency plane (s-IF), instead of the time-frequency (t-f) plane. It is shown that, in the s-IF plane, the fault components follow patterns that that are simple and unique for each type of fault, and thus does not depend on the way in which load and speed vary during the transient functioning; this characteristic makes the diagnostic task easier and more reliable. This work introduces a general scheme for the IMs diagnostic under transient conditions, through the analysis of the stator current in the s-IF plane. Another contribution of this paper is the introduction of the specific s-IF patterns associated with three different types of faults (rotor asymmetry fault, mixed eccentricity fault, and single-point bearing defects) that are theoretically justified and experimentally tested. As the calculation of the IF of the fault component is a key issue of the proposed diagnostic method, this paper also includes a comparative analysis of three different mathematical tools for calculating the IF, which are compared not only theoretically but also experimentally, comparing their performance when are applied to the tested diagnostic signals.This work was supported by the Spanish "Ministerio de Ciencia, Innovacion y Universidades (MCIU)", the "Agencia Estatal de Investigacion (AEI)" and the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I+D+i -Retos Investigacion 2018", project reference RTI2018-102175-B-I00 (MCIU/AEI/FEDER, UE).Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Burriel-Valencia, J.; Riera-Guasp, M. (2020). Fault Diagnosis in the Slip Frequency Plane of Induction Machines Working in Time-Varying Conditions. Sensors. 20(12):1-26. https://doi.org/10.3390/s20123398S126201

Enhanced Simulink Induction Motor Model for Education and Maintenance Training

[EN] The training of technicians in maintenance requires the use of signals produced by faulty machines in different operating conditions, which are difficult to obtain either from the industry or through destructive testing. Some tasks in electricity and control courses can also be complemented by an interactive induction machine model having a wider internal parameter configuration. This paper presents a new analytical model of induction machine under fault, which is able to simulate induction machines with rotor asymmetries and eccentricity in different load conditions, both stationary and transient states and yielding magnitudes such as currents, speed and torque. This model is faster computationally than the traditional method of simulating induction machine faults based on the Finite Element Method and also than other analytical models due to the rapid calculation of the inductances. The model is presented in Simulink by Matlab for the comprehension and interactivity with the students or lecturers and also to allow the easy combination of the effect of the fault with external influences, studying their consequences on a determined load or control system. An associated diagnosis tool is also presented.This work was supported by the Spanish Ministerio de Ciencia e Innovación under the framework of the Programa Nacional de Proyectos de Investigación Fundamental, Project Reference DPI2011-23740Pineda-Sanchez, M.; Climente Alarcón, V.; Riera-Guasp, M.; Puche-Panadero, R.; Pons Llinares, J. (2012). Enhanced Simulink Induction Motor Model for Education and Maintenance Training. Journal of Systemics, Cybernetics and Informatics. 10(2):92-97. http://hdl.handle.net/10251/105282S929710