Short-Frequency Fourier Transform for Fault Diagnosis of Induction Machines Working in Transient Regime

[EN] Transient-based methods for fault diagnosis of induction machines (IMs) are attracting a rising interest, due to their reliability and ability to adapt to a wide range of IM's working conditions. These methods compute the time-frequency (TF) distribution of the stator current, where the patterns of the related fault components can be detected. A significant amount of recent proposals in this field have focused on improving the resolution of the TF distributions, allowing a better discrimination and identification of fault harmonic components. Nevertheless, as the resolution improves, computational requirements (power computing and memory) greatly increase, restricting its implementation in low-cost devices for performing on-line fault diagnosis. To address these drawbacks, in this paper, the use of the short-frequency Fourier transform (SFFT) for fault diagnosis of induction machines working under transient regimes is proposed. The SFFT not only keeps the resolution of traditional techniques, such as the short-time Fourier transform, but also achieves a drastic reduction of computing time and memory resources, making this proposal suitable for on-line fault diagnosis. This method is theoretically introduced and experimentally validated using a laboratory test bench.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, under Project DPI2014-60881-R. The Associate Editor coordinating the review process was Dr. Edoardo Fiorucci.Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bañó, Á.; Pineda-Sanchez, M. (2017). Short-Frequency Fourier Transform for Fault Diagnosis of Induction Machines Working in Transient Regime. IEEE Transactions on Instrumentation and Measurement. 66(3):432-440. doi:10.1109/TIM.2016.2647458S43244066

Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window

[EN] The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in the transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration, the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as the analysis window. In this paper, the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15-MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current¿s spectrogram with a significant reduction of the required computational resourcesThis 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).Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Pineda-Sanchez, M. (2018). Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window. Sensors. 18(1):1-24. https://doi.org/10.3390/s18010146S12418

Diagnosis of electric induction machines in non-stationary regimes working in randomly changing conditions

Tradicionalmente, la detección de faltas en máquinas eléctricas se basa en el uso de la Transformada Rápida de Fourier ya que la mayoría de las faltas pueden ser diagnosticadas con ella con seguridad si las máquinas operan en condiciones de régimen estacionario durante un intervalo de tiempo razonable. Sin embargo, para aplicaciones en las que las máquinas operan en condiciones de carga y velocidad fluctuantes (condiciones no estacionarias) como por ejemplo los aerogeneradores, el uso de la Transformada Rápida de Fourier debe ser reemplazado por otras técnicas. La presente tesis desarrolla una nueva metodología para el diagnóstico de máquinas de inducción de rotor de jaula y rotor bobinado operando en condiciones no estacionarias, basada en el análisis de las componentes de falta de las corrientes en el plano deslizamiento frecuencia. La técnica es aplicada al diagnóstico de asimetrías estatóricas, rotóricas y también para la falta de excentricidad mixta. El diagnóstico de las máquinas eléctricas en el dominio deslizamiento-frecuencia confiere un carácter universal a la metodología ya que puede diagnosticar máquinas eléctricas independientemente de sus características, del modo en el que la velocidad de la máquina varía y de su modo de funcionamiento (motor o generador). El desarrollo de la metodología conlleva las siguientes etapas: (i) Caracterización de las evoluciones de las componentes de falta de asimetría estatórica, rotórica y excentricidad mixta para las máquinas de inducción de rotores de jaula y bobinados en función de la velocidad (deslizamiento) y la frecuencia de alimentación de la red a la que está conectada la máquina. (ii) Debido a la importancia del procesado de la señal, se realiza una introducción a los conceptos básicos del procesado de señal antes de centrarse en las técnicas actuales de procesado de señal para el diagnóstico de máquinas eléctricas. (iii) La extracción de las componentes de falta se lleva a cabo a través de tres técnicas de filtrado diferentes: filtros basados en la Transformada Discreta Wavelet, en la Transformada Wavelet Packet y con una nueva técnica de filtrado propuesta en esta tesis, el Filtrado Espectral. Las dos primeras técnicas de filtrado extraen las componentes de falta en el dominio del tiempo mientras que la nueva técnica de filtrado realiza la extracción en el dominio de la frecuencia. (iv) La extracción de las componentes de falta, en algunos casos, conlleva el desplazamiento de la frecuencia de las componentes de falta. El desplazamiento de la frecuencia se realiza a través de dos técnicas: el Teorema del Desplazamiento de la Frecuencia y la Transformada Hilbert. (v) A diferencia de otras técnicas ya desarrolladas, la metodología propuesta no se basa exclusivamente en el cálculo de la energía de la componente de falta sino que también estudia la evolución de la frecuencia instantánea de ellas, calculándola a través de dos técnicas diferentes (la Transformada Hilbert y el operador Teager-Kaiser), frente al deslizamiento. La representación de la frecuencia instantánea frente al deslizamiento elimina la posibilidad de diagnósticos falsos positivos mejorando la precisión y la calidad del diagnóstico. Además, la representación de la frecuencia instantánea frente al deslizamiento permite realizar diagnósticos cualitativos que son rápidos y requieren bajos requisitos computacionales. (vi) Finalmente, debido a la importancia de la automatización de los procesos industriales y para evitar la posible divergencia presente en el diagnóstico cualitativo, tres parámetros objetivos de diagnóstico son desarrollados: el parámetro de la energía, el coeficiente de similitud y los parámetros de regresión. El parámetro de la energía cuantifica la severidad de la falta según su valor y es calculado en el dominio del tiempo y en el dominio de la frecuencia (consecuencia de la extracción de las componentes de falta en el dominio de la frecuencia). El coeficiente de similitud y los parámetros de regresión son parámetros objetivos que permiten descartar diagnósticos falsos positivos aumentando la robustez de la metodología propuesta. La metodología de diagnóstico propuesta se valida experimentalmente para las faltas de asimetría estatórica y rotórica y para el fallo de excentricidad mixta en máquinas de inducción de rotor de jaula y rotor bobinado alimentadas desde la red eléctrica y desde convertidores de frecuencia en condiciones no estacionarias estocásticas.Vedreño Santos, FJ. (2013). Diagnosis of electric induction machines in non-stationary regimes working in randomly changing conditions [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34177TESI

Automatic Fault Diagnostic System for Induction Motors under Transient Regime Optimized with Expert Systems

[EN] Induction machines (IMs) power most modern industrial processes (induction motors) and generate an increasing portion of our electricity (doubly fed induction generators). A continuous monitoring of the machine's condition can identify faults at an early stage, and it can avoid costly, unexpected shutdowns of production processes, with economic losses well beyond the cost of the machine itself. Machine current signature analysis (MCSA), has become a prominent technique for condition-based maintenance, because, in its basic approach, it is non-invasive, requires just a current sensor, and can process the current signal using a standard fast Fourier transform (FFT). Nevertheless, the industrial application of MCSA requires well-trained maintenance personnel, able to interpret the current spectra and to avoid false diagnostics that can appear due to electrical noise in harsh industrial environments. This task faces increasing difficulties, especially when dealing with machines that work under non-stationary conditions, such as wind generators under variable wind regime, or motors fed from variable speed drives. In these cases, the resulting spectra are no longer simple one-dimensional plots in the time domain; instead, they become two-dimensional images in the joint time-frequency domain, requiring highly specialized personnel to evaluate the machine condition. To alleviate these problems, supporting the maintenance staff in their decision process, and simplifying the correct use of fault diagnosis systems, expert systems based on neural networks have been proposed for automatic fault diagnosis. However, all these systems, up to the best knowledge of the authors, operate under steady-state conditions, and are not applicable in a transient regime. To solve this problem, this paper presents an automatic system for generating optimized expert diagnostic systems for fault detection when the machine works under transient conditions. The proposed method is first theoretically introduced, and then it is applied to the experimental diagnosis of broken bars in a commercial cage induction motor.Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Pineda-Sanchez, M.; Pérez-Cruz, J.; Riera-Guasp, M. (2019). Automatic Fault Diagnostic System for Induction Motors under Transient Regime Optimized with Expert Systems. Electronics. 8(1):1-16. https://doi.org/10.3390/electronics8010006S11681Puche-Panadero, R., Pineda-Sanchez, M., Riera-Guasp, M., Roger-Folch, J., Hurtado-Perez, E., & Perez-Cruz, J. (2009). Improved Resolution of the MCSA Method Via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip. IEEE Transactions on Energy Conversion, 24(1), 52-59. doi:10.1109/tec.2008.2003207Abd-el -Malek, M., Abdelsalam, A. K., & Hassan, O. E. (2017). Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform. Mechanical Systems and Signal Processing, 93, 332-350. doi:10.1016/j.ymssp.2017.02.014Martinez, J., Belahcen, A., & Muetze, A. (2017). Analysis of the Vibration Magnitude of an Induction Motor With Different Numbers of Broken Bars. IEEE Transactions on Industry Applications, 53(3), 2711-2720. doi:10.1109/tia.2017.2657478Sapena-Bano, A., Pineda-Sanchez, M., Puche-Panadero, R., Perez-Cruz, J., Roger-Folch, J., Riera-Guasp, M., & Martinez-Roman, J. (2015). Harmonic Order Tracking Analysis: A Novel Method for Fault Diagnosis in Induction Machines. IEEE Transactions on Energy Conversion, 30(3), 833-841. doi:10.1109/tec.2015.2416973Sapena-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.2016.2626008Burriel-Valencia, J., Puche-Panadero, R., Martinez-Roman, J., Sapena-Bano, A., & Pineda-Sanchez, M. (2017). Short-Frequency Fourier Transform for Fault Diagnosis of Induction Machines Working in Transient Regime. IEEE Transactions on Instrumentation and Measurement, 66(3), 432-440. doi:10.1109/tim.2016.2647458Yin, Z., & Hou, J. (2016). Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes. Neurocomputing, 174, 643-650. doi:10.1016/j.neucom.2015.09.081Bazan, G. H., Scalassara, P. R., Endo, W., Goedtel, A., Godoy, W. F., & Palácios, R. H. C. (2017). Stator fault analysis of three-phase induction motors using information measures and artificial neural networks. Electric Power Systems Research, 143, 347-356. doi:10.1016/j.epsr.2016.09.031Mustafidah, H., Hartati, S., Wardoyo, R., & Harjoko, A. (2014). Selection of Most Appropriate Backpropagation Training Algorithm in Data Pattern Recognition. International Journal of Computer Trends and Technology, 14(2), 92-95. doi:10.14445/22312803/ijctt-v14p120Godoy, W. F., da Silva, I. N., Lopes, T. D., Goedtel, A., & Palácios, R. H. C. (2016). Application of intelligent tools to detect and classify broken rotor bars in three-phase induction motors fed by an inverter. IET Electric Power Applications, 10(5), 430-439. doi:10.1049/iet-epa.2015.0469Ghorbanian, V., & Faiz, J. (2015). A survey on time and frequency characteristics of induction motors with broken rotor bars in line-start and inverter-fed modes. Mechanical Systems and Signal Processing, 54-55, 427-456. doi:10.1016/j.ymssp.2014.08.022Valles-Novo, R., de Jesus Rangel-Magdaleno, J., Ramirez-Cortes, J. M., Peregrina-Barreto, H., & Morales-Caporal, R. (2015). Empirical Mode Decomposition Analysis for Broken-Bar Detection on Squirrel Cage Induction Motors. IEEE Transactions on Instrumentation and Measurement, 64(5), 1118-1128. doi:10.1109/tim.2014.2373513De Santiago-Perez, J. J., Rivera-Guillen, J. R., Amezquita-Sanchez, J. P., Valtierra-Rodriguez, M., Romero-Troncoso, R. J., & Dominguez-Gonzalez, A. (2018). Fourier transform and image processing for automatic detection of broken rotor bars in induction motors. Measurement Science and Technology, 29(9), 095008. doi:10.1088/1361-6501/aad3aaMerabet, H., Bahi, T., Drici, D., Halam, N., & Bedoud, K. (2017). Diagnosis of rotor fault using neuro-fuzzy inference system. Journal of Fundamental and Applied Sciences, 9(1), 170. doi:10.4314/jfas.v9i1.12Riera-Guasp, M., Pineda-Sanchez, M., Perez-Cruz, J., Puche-Panadero, R., Roger-Folch, J., & Antonino-Daviu, J. A. (2012). Diagnosis of Induction Motor Faults via Gabor Analysis of the Current in Transient Regime. IEEE Transactions on Instrumentation and Measurement, 61(6), 1583-1596. doi:10.1109/tim.2012.2186650Gyftakis, K. N., Marques Cardoso, A. J., & Antonino-Daviu, J. A. (2017). Introducing the Filtered Park’s and Filtered Extended Park’s Vector Approach to detect broken rotor bars in induction motors independently from the rotor slots number. Mechanical Systems and Signal Processing, 93, 30-50. doi:10.1016/j.ymssp.2017.01.04

Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

[EN] Induction machines drive many industrial processes and their unexpected failure can cause heavy producti on losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, which is not the case for wind generators, motors driving varying loads, and so forth. In these cases, an analysis in the time-frequency domain¿such as a spectrogram¿is required for detecting faults signatures. The spectrogram is built using the short time Fourier transform, but its resolution depends critically on the time window used to generate it¿short windows provide good time resolution but poor frequency resolution, just the opposite than long windows. Therefore, the window must be adapted at each time to the shape of the expected fault harmonics, by highly skilled maintenance personnel. In this paper this problem is solved with the design of a new multi-band window, which generates simultaneously many different narrow-band current spectrograms and combines them into as single, high resolution one, without the need of manual adjustments. The proposed method is validated with the diagnosis of bar breakages during the start-up of a commercial induction motor.This research was funded 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).Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Riera-Guasp, M.; Sapena-Bano, A.; Pineda-Sanchez, M. (2019). Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines. Energies. 12(17):1-18. https://doi.org/10.3390/en12173361S118121

Fault Diagnosis of Rotating Electrical Machines in Transient Regime Using a Single Stator Current's FFT

© 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] The discrete wavelet transform (DWT) has attracted a rising interest in recent years to monitor the condition of rotating electrical machines in transient regime, because it can reveal the time-frequency behavior of the current's components associated to fault conditions. Nevertheless, the implementation of the wavelet transform (WT), especially on embedded or low-power devices, faces practical problems, such as the election of the mother wavelet, the tuning of its parameters, the coordination between the sampling frequency and the levels of the transform, and the construction of the bank of wavelet filters, with highly different bandwidths that constitute the core of the DWT. In this paper, a diagnostic system using the harmonic WT is proposed, which can alleviate these practical problems because it is built using a single fast Fourier transform of one phase's current. The harmonic wavelet was conceived to perform musical analysis, hence its name, and it has spread into many fields, but, to the best of the authors' knowledge, it has not been applied before to perform fault diagnosis of rotating electrical machines in transient regime using the stator current. The simplicity and performance of the proposed approach are assessed by comparison with other types of WTs, and it has been validated with the experimental diagnosis of a 3.15-MW induction motor with broken bars.This work was supported by the Spanish Ministerio de Ciencia e Innovacion through the Programa Nacional de Proyectos de Investigacion Fundamental under Project DPI2011-23740. The Associate Editor coordinating the review process was Dr. Ruqiang Yan.Sapena-Bano, A.; Pineda-Sanchez, M.; Puche-Panadero, R.; Martinez-Roman, J.; Matic, D. (2015). Fault Diagnosis of Rotating Electrical Machines in Transient Regime Using a Single Stator Current's FFT. IEEE Transactions on Instrumentation and Measurement. 64(11):3137-3146. https://doi.org/10.1109/TIM.2015.2444240S31373146641

Harmonic Order Tracking Analysis: A Speed-Sensorless Method for Condition Monitoring of Wound Rotor Induction Generators

"(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works."[EN] This paper introduces a speed-sensorless method for detecting rotor asymmetries in wound rotor induction machines working under nonstationary conditions. The method is based on the time-frequency analysis of rotor currents and on a subsequent transformation, which leads to the following goals: unlike conventional spectrograms, it enables to show the diagnostic results as a simple graph, similar to a Fourier spectrum, but where the fault components are placed always at the same positions, regardless the working conditions of the machine; moreover, it enables to assess the machine condition through a very small set of parameters. These characteristics facilitate the understanding and processing of the diagnostic results, and thus, help to design improved monitoring and predictive maintenance systems. Also these features make the proposed method very suitable for condition monitoring of wind power generators, because it fits well with the usual non stationaryworking conditions ofwind turbines, and makes feasible the transmission of significant diagnostic information to the remote monitoring center using standard data transmission systems. Simulation results and experimental tests, carried out on a 5-kW laboratory rig, show the validity of the proposed method and illustrate its advantages regarding previously developed diagnostic methods under nonstationary conditions.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 under Project (DPI2014-60881-R).Sapena Bañó, Á.; Riera Guasp, M.; Puche Panadero, R.; Martínez Román, JA.; Pérez Cruz, J.; Pineda Sánchez, M. (2016). Harmonic Order Tracking Analysis: A Speed-Sensorless Method for Condition Monitoring of Wound Rotor Induction Generators. IEEE Transactions on Industry Applications. 52(6):4719-4729. https://doi.org/10.1109/TIA.2016.2597134S4719472952

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

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