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[EN] The locally optimized chirplet spectrogram (LOCS) is a novel method proposed in this work for generating a high-resolution and cost-effective spectrogram of the induction machine (IM) current, suitable for the identification of fault-related harmonics in transient conditions. Its distinctive novelty is that it optimizes automatically the parameters of the analysing window used for building the current spectrogram, at each point of the time-frequency plane, with the computational cost of a conventional, non-optimized spectrogram. It is based on the definition of a large dictionary of different chirplet windows, which are combined into a single, complex time window. A single short time Fourier transform with this new window generates in parallel the spectrograms of all the dictionary windows, and the LOCS chooses among them the locally optimized values in an automatic way. The proposed technique is applied to the diagnosis of two commercial induction motors with bar breakages and mixed eccentricity faults.

[EN] Induction machines (IMs) are one of the main sources of mechanical power in many industrial processes, especially squirrel cage IMs (SCIMs), due to their robustness and reliability. Their sudden stoppage due to undetected faults may cause costly production breakdowns. One of the most frequent types of faults are cage faults (bar and end ring segment breakages), especially in motors that directly drive high-inertia loads (such as fans), in motors with frequent starts and stops, and in case of poorly manufactured cage windings. A continuous monitoring of IMs is needed to reduce this risk, integrated in plant-wide condition based maintenance (CBM) systems. Diverse diagnostic techniques have been proposed in the technical literature, either data-based, detecting fault-characteristic perturbations in the data collected from the IM, and model-based, observing the differences between the data collected from the actual IM and from its digital twin model. In both cases, fast and accurate IM models are needed to develop and optimize the fault diagnosis techniques. On the one hand, the finite elements approach can provide highly accurate models, but its computational cost and processing requirements are very high to be used in on-line fault diagnostic systems. On the other hand, analytical models can be much faster, but they can be very complex in case of highly asymmetrical machines, such as IMs with multiple cage faults. In this work, a new method is proposed for the analytical modelling of IMs with asymmetrical cage windings using a tensor based approach, which greatly reduces this complexity by applying routine tensor algebra to obtain the parameters of the faulty IM model from the healthy one. This winding tensor approach is explained theoretically and validated with the diagnosis of a commercial IM with multiple cage faults.

[EN] Over the years, induction machines (IMs) have become key components in industry applications as mechanical power sources (working as motors) as well as electrical power sources (working as generators). Unexpected breakdowns in these components can lead to unscheduled down time and consequently to large economic losses. As breakdown of IMs for failure study is not economically feasible, several IM computer models under faulty conditions have been developed to investigate the characteristics of faulty machines and have allowed reducing the number of destructive tests. This paper provides a review of the available techniques for faulty IMs modelling. These models can be categorised as models based on electrical circuits, on magnetic circuits, models based on numerical methods and the recently proposed in the technical literature hybrid models or models based on finite element method (FEM) analytical techniques. A general description of each type of model is given with its main benefits and drawbacks in terms of accuracy, running times and ability to reproduce a given fault

[EN] Since it is not efficient to physically study many machine failures, models of faulty induction machines (IMs) have attracted a rising interest. These models must be accurate enough to include fault effects and must be computed with relatively low resources to reproduce different fault scenarios. Moreover, they should run in real time to develop online condition-monitoring (CM) systems. Hybrid finite element method (FEM)-analytical models have been recently proposed for fault diagnosis purposes since they keep good accuracy, which is widely accepted, and they can run in real-time simulators. However, these models still require the full simulation of the FEM model to compute the parameters of the analytical model for each faulty scenario with its corresponding computing needs. To address these drawbacks (large computing power and memory resources requirements) this paper proposes sparse identification techniques in combination with the trigonometric interpolation polynomial for the computation of IM model parameters. The proposed model keeps accuracy similar to a FEM model at a much lower computational effort, which could contribute to the development and to the testing of condition-monitoring systems. This approach has been applied to develop an IM model under static eccentricity conditions, but this may extend to other fault types.

[EN] Induction machines (IMs) are essential components of many industrial installations and, therefore, their faults must be detected early. Fault detection using current spectrum analysis is attracting an increasing interest as a condition-based monitoring (CBM) technique. However, its use to detect rotor asymmetries in high-power IMs, which operate at very low slip, is particularly challenging, due to the closeness of the characteristic fault harmonics to the fundamental component, separated only a few MHz. Their reliable detection in harsh industrial environments requires a very high spectral resolution, that is, long acquisition times and a huge number of current samples, which hinders its implementation on embedded, online devices with limited computing resources. To address this problem, this article presents a novel combination of diagnostic techniques, the use of the rectified current as diagnostic signal, and the Goertzel algorithm as signal processing tool. This unique combination allows for an optimized implementation of the Goertzel algorithm, which provides a high spectral resolution in the full load range of the machine, with a low computational cost and a negligible memory footprint. This proposal is validated experimentally with the fault diagnosis of a high-power medium-voltage industrial motor.

(c) 2020 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] Fault diagnosis of induction motors through the analysis of the stator current is increasingly being used in maintenance systems, because it is non-invasive and has low requirements of hardware and software. Nevertheless, its industrial application faces some practical limitations. In particular, the detection of fault harmonics that are very close to the fundamental component is challenging, as in large induction motors working at very low slip, because the leakage of the fundamental can hide the fault components until the damage is severe. Several methods have been proposed to alleviate this problem, although all of them increase noticeably the complexity of the diagnostic system. In this paper, a novel method is proposed, based on the analysis of the rectified motor current. It is shown that its spectrum contains the same fault harmonics as the spectrum of the original current signal, but with a much lower frequency, and free from the fundamental component leakage. Besides, the proposed method is very easy to implement, either by software, using the absolute value of the current samples, or by hardware, using a simple rectifier. The proposed approach is presented theoretically and validated experimentally with the detection of a broken bars fault of a large induction motor.

[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 asymmet

[EN] Induction machines (IMs) are critical components of many industrial processes, what justifies the use of condition-based maintenance (CBM) systems for detecting their faults at an early stage, in order to avoid costly breakdowns of production lines. The development of CBM systems for IMs relies on the use of fast models that can accurately simulate the machine in faulty conditions. In particular, IM models must be able to reproduce the characteristic harmonics that the IM faults impress in the spatial waves of the air gap magneto-motive force (MMF), due to the complex interactions between spatial and time harmonics. A common type of fault is the eccentricity of the rotor core, which provokes an unbalanced magnetic pull, and can lead to destructive rotor-stator rub. Models developed using the finite element method (FEM) can achieve the required accuracy, but their high computational costs hinder their use in online CBM systems. Analytical models are much faster, but they need an inductance matrix that takes into account the asymmetries generated by the eccentricity fault. Building the inductance matrix for eccentric IMs using traditional techniques, such as the winding function approach (WFA), is a highly complex task, because these functions depend on the combined effect of the winding layout and of the air gap asymmetry. In this paper, a novel method for the fast and simple computation of the inductance matrix for eccentric IMs is presented, which decouples the influence of the air gap asymmetry and of the winding configuration using two independent tensors. It is based on the construction of a primitive inductance tensor, which formulates the eccentricity fault using single conductors as the simplest reference frame; and a winding tensor that converts it into the inductance matrix of a particular machine, taking into account the configuration of the windings. The proposed approach applies routine procedures from tensor algebra for performing such transformati

[EN] Condition based maintenance (CBM) systems of induction machines (IMs) require fast and accurate models that can reproduce the fault related harmonics generated by different kinds of faults. Such models are needed to develop new diagnostic algorithms for detecting the faults at an early stage, to analyse the physical interactions between simultaneous faults of different types, or to train expert systems that can supervise and identify these faults in an autonomous way. To achieve these goals, these models must take into account the space harmonics of the air gap magnetomotive force (MMF) generated by the machine windings under fault conditions, due to the complex interactions between spatial and time harmonics in a faulty machine. One of the most common faults in induction machines is the rotor eccentricity, which can cause significant radial forces and, in extreme cases, produce destructive rotor-stator rub. However, the development of a fast, analytical model of the eccentric IM is a challenging task, due to the non-uniformity of the air gap. In this paper, a new method is proposed to obtain such a fast model. This method, which is theoretically justified, first enables a fast calculation of the self and mutual inductances of the stator and rotor phases for every rotor position, taking into account the non-uniform air-gap length and the actual position of all the stator and rotor conductors. Once these inductances are calculated, they are used in a coupled circuits analytical model of the IM, which in this way is able to calculate the time evolution of the electrical and mechanical quantities that characterize the machine functioning, under any type of eccentricity. Specifically, the model is able to reproduce accurately the characteristic eccentricity fault related harmonics in the spectrum of the stator current. The proposed approach is validated through two different methods. First, using a finite elements (FEM) model, in order to validate the correctness o

[EN] This paper presents an application for the energy monitoring of commercial refrigeration facility, based on the concept and technology of the Internet of Things. The purpose of this application is to offer support to the activities of energy audits ant to the implementation of energy management systems, where the energy consumption of a facility must be characterized, or the saving measures applied must be supervised. Among the main features offered by this application are the possibility of remotely monitoring the operating conditions of a refrigeration cycle, as well as its energy consumption. Likewise, its Internet connectivity allows the monitoring system to be scaled up and to group the measurements of several refrigeration installations, which transforms this application into a powerful tool for benchmarking. Internet access also allows notifications via e mail under particular conditions of the operation of any of the facilities. On the other hand, the application has the capacity to collect and to store in a database the measurements taken by the field devices, allowing the generation of historical reports showing the evolution of the operation of the installation. The main component of this application is the industrial gateway, SIMATIC IOT2040, which is used to read industrial communication buses and to connect to the Internet. Likewise, the open source software Node RED has been the programming platform used for data reading and processing.

(c) 2019 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] While wind turbine (WT) power capacities continue to increase and new offshore developments are being deployed, operation and maintenance (O&M) costs continue to rise, becoming the center of attention in the wind energy sector. The electric generator is among the top three contributors to failure rates and downtime of WTs, where the doubly fed induction generator (DFIG) is the dominant technology among variable speed WTs. Thus, the early detection of generator faults, which can be achieved through predictive maintenance, is vital in order to reduce O&M costs. The goal of this paper is to analyze a long-term monitoring campaign of an in-service WT equipped with a DFIG. A novel method named the harmonic order tracking analysis is used with two main objectives: first, to facilitate the data interpretation for non-trained maintenance personnel, and second, to reduce the amount of data that must be stored and transferred for the diagnosis of the DFIG. This method is applied and validated for the first time on an operating WT.

[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.

[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 theoret

[EN] Most industrial processes are run by induction machines (IMs). Condition monitoring of IM assures their continuity of service, and it may avoid highly costly breakdowns. Among the methods for condition monitoring, online motor current signature analysis is being attracting a rising interest, because it is non-invasive, and it can identify a wide variety of faults at early stage. To favour the development of on-line fault diagnosis techniques, it is necessary to have real-time currents with which test the new techniques and devices. Models running in real time in hardware-in-the-loop (HIL) simulators are a suitable alternative to balance the drawbacks of test benches (costly, limited machines, faults and working conditions). These models must be accurate enough to reflect the effects of a fault and they must be running in real time. A promising technique based on the equivalent circuit parameters calculation of IM by finite element analysis (FEA) is attracting a rising interest due to its reliability, performance and the possibility of being run in a HIL. Nevertheless, prior to running in a HIL, it is necessary to compute the IM parameters using FEA, which requires long simulation times and high computing resources. Consequently, covering a whole range of degrees of a giving fault could be unaffordable. What is proposed in this paper is to apply the sparse subspace learning (SSL) in combination with the hierarchical Lagrangian interpolation (HLI) to obtain the parametric solutions of the faulty IM model that cover the whole range of severity of a given fault, with a reduced number of FEA simulations. By means of this approach it is possible not only to boost the computation speed but also to achieve a significant reduction of memory requirements while retaining reasonable accuracy compared to traditional FEA, so enabling the real-time simulation of predictive models.

[EN] Fault diagnosis of rotor asymmetries of induction machines (IMs) using the stator current relies on the detection of the characteristic signatures of the fault harmonics in the current spectrum. In some scenarios, such as large induction machines running at a very low slip, or unloaded machines tested offline, this technique may fail. In these scenarios, the fault harmonics are very close to the frequency of the fundamental component, and have a low amplitude, so that they may remain undetected, buried under the fundamental's leakage, until the damage is severe. To avoid false positives, a proven approach is to search for the fault harmonics in the current envelope, instead of the current itself, because in this case the spectrum is free from the leakage of the fundamental. Besides, the fault harmonics appear at a very low frequency. Nevertheless, building the current spectrum is costly in terms of computing complexity, as in the case of the Hilbert transform, or hardware resources, as in the need for simultaneously sampling three stator currents in the case of the extended current Park's vector approach (EPVA). In this work, a novel method is proposed to avoid this problem. It is based on sampling a phase current just twice per current cycle, with a fixed delay with respect to its zero crossings. It is shown that the spectrum of this reduced set of current samples contains the same fault harmonics as the spectrum of the full-length current envelope, despite using a minimal amount of computing resources. The proposed approach is cost-effective, because the computational requirements for building the current envelope are reduced to less than 1% of those required by other conventional methods, in terms of storage and computing time. In this way, it can be implemented with low-cost embedded devices for on-line fault diagnosis. The proposed approach is introduced theoretically and validated experimentally, using a commercial induction motor with a broken bar under

[ES] Las asignaturas a nivel de grado de Máquinas Eléctricas frecuentemente incluyen una introducción al campo magnético de entrehierro como elemento básico en la conversión electromecánica de energía incluyendo resultados de aprendizaje como reconocer las propiedades del campo magnético de entrehierro producido por diferentes devanados y su correlación con las características de la corriente del devanado: amplitud y frecuencia. Este artículo describe el diseño de un nuevo banco de ensayos orientado a facilitar la adquisición de esos resultados de aprendizaje. El banco está diseñado en torno a cuatro elementos principales: una máquina de anillos rozantes modificada, una fuente de corriente múltiple ajustable, 2 tarjetas de adquisición de datos y un Instrumento Virtual basado en un PC. Este Instrumento Virtual proporciona a la fuente de corriente las referencias de corriente adecuadas al modo de operación elegido por el usuario (corriente mono- o trifásica continua o alterna) y mide y representa el campo magnético de entrehierro para ese modo de operación. El trabajo con el banco se estructura en una serie de experimentos orientados a reforzar y alcanzar los resultados de aprendizaje deseados. Se ha constatado una mejora del aprendizaje significativa tras la implantación de este equipo en las prácticas de laboratorio, [EN] Undergraduate courses in electrical machines often include an introduction to the air gap magnetic field as a basic element in the energy conversion process. The students must learn the main properties of the field produced by basic winding configurations and how they relate to the winding current and frequency. This paper describes a new test equipment design aimed at helping students achieve these learning goals. The test equipment is designed around four main elements: a modified slip ring induction machine, a winding current driver board, the DAQ boards and a PC-based virtual instrument. The virtual instrument provides the winding current drivers with the suitable current references depending on the user selected machine operational status (single or three phase/winding with DC or AC current) and measures and displays the air gap magnetic field for that operational status. Students¿ laboratory work is organized into a series of experiments that guide their achievement of these air gap field-related abilities. Student learning, assessed by means of end of semester exams and pre-&-post lab exams has increased significantly. Students¿ opinions of the relevance, usefulness and motivational effect of the laboratory was also positive.

[EN] Fault diagnosis of induction machines (IMs) requires a fast model of the machine, for adjusting fault thresholds in data-driven diagnostic methods, for computing the residuals in model-driven diagnostic systems, or for training autonomous expert systems. Due to the interaction between time and space harmonics under faulty conditions, this model must simulate very accurately the space harmonics of the air gap magnetomotive force (MMF) generated by the machine's windings. But the computation of the phases' inductances, taking into account the spatial harmonics of the MMF, for every angular position of the rotor, and under non-symmetrical, faulty conditions, is a time-consuming task in IMs' models. In this paper, a very fast method for obtaining the inductances of rotating electrical machines is proposed, based on a single discrete circular convolution. With the proposed approach, the mutual inductances of two phases, taking into account the spatial harmonics of the air gap MMF, are calculated for every relative angular position using a single equation, solved with the fast Fourier transform (FFT). Asymmetrical winding distributions, and the linear rise of the air gap MMF across skewed slots are easily modeled without increasing the computation time. The proposed method is introduced theoretically and validated with an experimental test-bed using commercial induction motors with forced broken bars faults.

[EN] The development of advanced fault diagnostic systems for induction machines through the stator current requires accurate and fast models that can simulate the machine under faulty conditions, both in steady-state and in transient regime. These models are far more complex than the models used for healthy machines, because one of the effect of the faults is to change the winding configurations (broken bar faults, rotor asymmetries, and inter-turn short circuits) or the magnetic circuit (eccentricity and bearing faults). This produces a change of the self and mutual phase inductances, which induces in the stator currents the characteristic fault harmonics used to detect and to quantify the fault. The development of a machine model that can reflect these changes is a challenging task, which is addressed in this work with a novel approach, based on the concept of partial inductances. Instead of developing the machine model based on the phases¿ coils, it is developed using the partial inductance of a single conductor, obtained through the magnetic vector potential, and combining the partial inductances of all the conductors with a fast Fourier transform for obtaining the phases¿ inductances. The proposed method is validated using a commercial induction motor with forced broken bars

[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 resources

[EN] Improved fault diagnostic techniques in induction generators is a field of growing interest given the negative impact * that unexpected breakdowns have on energy production and on the electrical system. New diagnostic techniques based on induction generator currents monitoring have recently been developed, but their use is still irrelevant despite the advantages that presents to detect electrical faults in the generator. This situation is due to the needs of high computing power and memory resources which are not available in embedded devices for on-line monitoring, also, to the use of signal processing techniques that generate volumes of data difficult to transfer to control centres, where they could be processed. This paper proposes the use of a recent methodology known as the harmonic order tracking analysis (HOTA) that solve these problems to for the diagnosis of induction generators. This approach can be implemented in low cost digital devices; the resultant patterns are very simple and easily interpretable, even by nonqualified personnel. Moreover, these patterns are characterized by a very low number of parameters, which make easy their transmission to remote control centres. In this paper the practical application of this approach is proposed using a laboratory test bed.

[EN] AC lines of industrial busbar systems are usually built using conductors with rectangular cross sections, where each phase can have several parallel conductors to carry high currents. The current density in a rectangular conductor, under sinusoidal conditions, is not uniform. It depends on the frequency, on the conductor shape, and on the distance between conductors, due to the skin effect and to proximity effects. Contrary to circular conductors, there are not closed analytical formulas for obtaining the frequency-dependent impedance of conductors with rectangular cross-section. It is necessary to resort to numerical simulations to obtain the resistance and the inductance of the phases, one for each desired frequency and also for each distance between the phases' conductors. On the contrary, the use of the parametric proper generalized decomposition (PGD) allows to obtain the frequency-dependent impedance of an AC line for a wide range of frequencies and distances between the phases' conductors by solving a single simulation in a 4D domain (spatial coordinates x and y, the frequency and the separation between conductors). In this way, a general "virtual chart" solution is obtained, which contains the solution for any frequency and for any separation of the conductors, and stores it in a compact separated representations form, which can be easily embedded on a more general software for the design of electrical installations. The approach presented in this work for rectangular conductors can be easily extended to conductors with an arbitrary shape.

[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.