J. Roger-Folch

Polytechnical University of Valencia, Valenza, Valencia, Spain

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Publications (47)38.48 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper researches the detection of mixed eccentricity in Inverter-Fed Induction Motors. The classic FFT method cannot be applied when the stator current captured is not in steady state, which is very common in these motors. Therefore, a transform able to detect the time–frequency evolutions of the components present in the transient signal captured must be applied. In order to optimize the result, a method to calculate the theoretical time–frequency evolution of the stator current components is presented, using only the captured current. This previously obtained information enables the use of the proposed transform: the Adaptive Slope Transform, based on appropriately choosing the atom slope in each point analyzed. Thanks to its adaptive characteristics, the time–frequency evolution of the main components in a stator transient current is traced precisely and with high detail in the 2D time–frequency plot obtained. As a consequence, the time–frequency plane characteristic patterns produced by the Eccentricity Related Harmonics are easily and clearly identified enabling a reliable diagnosis. Moreover, the problem of quantifying the presence of the fault is solved presenting a simple and easy to apply method. The transform capabilities have been shown successfully diagnosing an Inverter-Fed Induction Motor with mixed eccentricity during a startup, a decrease in the assigned frequency, and a load variation with and without slip compensation.
    Mechanical Systems and Signal Processing 10/2014; · 1.91 Impact Factor
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    ABSTRACT: This work presents an approach that enables, among other, to compute the energy balance and heating profiles of induction motors under different fault conditions. The method is based on a simplified mathematical model, based on the First Law of Thermodynamics that relies on the motor geometrical dimensions as well as on the temperature values provided by infrared thermography measurements. To check its applicability, a stepwise methodology is designed; the technique is tested by comparing the thermal behavior of two 1.1kW induction motors, one of them healthy while the other presenting a forced bearing damage. Their respective thermal evolutions are analyzed, relying on temperature measurements on the motor frame that are obtained from infrared images registered during complete startups of the motor. These experimental values are compared with the calculated ones using the simplified mathematical model. Despite the simplifications performed, the results reflect a high accuracy between the experimental and the calculated values, enabling to compare motors under different faulty conditions. Additionally, the results obtained confirm the potential of this methodology to perform predictive diagnosis in induction motors.
    11th International Conference on Modeling and Simulation of Electric Machines, Converters and Systems (ELECTRIMACS 2014), Valencia (SPAIN); 05/2014
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    ABSTRACT: On-line diagnosis of induction motors faults requires special, high speed hardware, such as DSP or FPGAs. Practical implementation of diagnosis algorithms in such a device must take into account the limited amount of memory available for storing sampled data, and for performing spectral analysis using the FFT. Another practical problem is the need to filter the mains component, whose leakage can hide fault harmonics, prior to compute the FFT of the current's signal. This requires the use of digital filters, that must be tuned in case of using variable speed drives that can operate the motor at different speeds. In this paper, an advanced demodulation technique that is able to eliminate the mains component with an extremely low memory requirement, based on the Teager- Kaiser energy operator, is presented. The demodulated current is footprint is down sampled, so that only 2kb of memory are needed to perform the diagnosis process. The proposed method is implemented in a DSP commercial board online diagnosis system and tested on commercial induction motors with broken bars. Finally, the results are compared with the results obtained offline using conventional Motor Current Signature Analysis method.
    Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), 2013 9th IEEE International Symposium on; 01/2013
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    ABSTRACT: Infrared thermography is a technique that has been frequently used as a predictive tool for electrical installations maintenance, since many of the failures or installation defects lead to temperature increments in specific points or areas. However, its application to fault detection in electric motors is far less usual. Alternative techniques, based on current or vibration monitoring, are still preferred, despite the analyses of these quantities do not enable the diagnosis of a significant number of failures that occur in these machines. In this regard, infrared data may provide very useful information for the detection of some faults which are not easy to be detected with currents or vibrations. In addition, this can be done in a non-invasive way, i.e., without interfering with the machine operation. The spectacular evolution undergone by the infrared cameras, which even enable the capture of motor thermal transients, represented by sequences of high resolution images as well as the monitoring of the temperature evolution at any point of the surface, is a fact that contributes to the great potential of this technique.In this work, a new methodology relying on the combination between the heat transfer theory and infrared data is proposed. The main pursued objectives are: (1) to study the thermal behaviour and perform the energy balance by building a thermal model of the induction motor using infrared data and (2) to set the baseline for further complex failure diagnosis in electric motors.To achieve these goals, a 1.1 kW induction motor is tested; thermography images of the motor frame are captured every second during the whole startup transient, from standstill till steady-state, by using an infrared camera connected to a laptop computer fitted with an acquisition and analysis software. These infrared data are used in a first stage to build the thermal model of the induction motor. Afterwards, in a second stage, the obtained results are compared with those corresponding to faulty machines to study the applicability of thermography data for diagnosis purposes.The results prove the potential of the approach to become a powerful diagnosis tool, complementing the information provided by techniques relying on other quantities, in cases in which they are not conclusive.
    Engineering Failure Analysis 01/2013; 35:178–192. · 1.13 Impact Factor
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    ABSTRACT: The diagnosis of induction motors through the spectral analysis of the stator current allows for the online identification of different types of faults. One of the major difficulties of this method is the strong influence of the mains component of the current, whose leakage can hide fault harmonics, especially when the machine is working at very low slip. In this paper, a new method for demodulating the stator current prior to its spectral analysis is proposed, using the Teager-Kaiser energy operator. This method is able to remove the mains component of the current with an extremely low usage of computer resources, because it operates just on three consecutive samples of the current. Besides, this operator is also capable of increasing the signal-to-noise ratio of the spectrum, sharpening the spectral peaks that reveal the presence of the faults. The proposed method has been deployed to a PC-based offline diagnosis system and tested on commercial induction motors with broken bars, mixed eccentricity, and single-point bearing faults. The diagnostic results are compared with those obtained through the conventional motor current signature analysis method.
    IEEE Transactions on Energy Conversion 01/2013; 28(4):1036-1044. · 3.35 Impact Factor
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    ABSTRACT: The use of advanced diagnosis techniques for induction motor (IM) faults relies on the use of automated classifiers, such as those based on support vector machines (SVMs), which are able to assess the condition of the machine using a set of relevant features extracted either from the time domain or from the frequency domain machines signals. But the performance of such systems depends on two main factors: the quantity that is used to obtain the machine's condition, and the signal processing tool used for extract the features set. In this paper, a combination of the most used quantities and signal processing tools is used for diagnosis a set of machines with broken bars, fed from the mains and from variable speed drives, using the same SVM. In this way, the most efficient combination can be chosen, from the point of view of the performance of the automatic classifier system.
    Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), 2013 9th IEEE International Symposium on; 01/2013
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    ABSTRACT: This paper is focused on fault diagnostic methodologies suited for induction machines which are subjected to fluctuating loads and speeds during their normal duty cycle. In these cases, the load and speed fluctuate continuously, near their rated values, and in a random way; the diagnosis becomes more complicated than under stationary regime or under other kind of transients, as the startup, much intensively studied at the literature, in which the speed, slip, and current undergo larger variations and evolve in a predefined way. In the first part of this paper, different approaches which enable the diagnosis under non-stationary conditions are introduced from a conceptual perspective. The second part of this work is devoted to the practical implementation of the diagnostic approaches previously introduced: the suitable signal analysis tools needed for carrying out diverse diagnostic approaches are introduced in a comprehensive way and supported by experimental results.
    Electrical Machines Design Control and Diagnosis (WEMDCD), 2013 IEEE Workshop on; 01/2013
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    ABSTRACT: Failures in damper windings of synchronous machines operating in real facilities have been recently reported by several authors and companies. These windings are crucial elements of synchronous motors and generators, playing an important role in the asynchronous startup of these machines as well as in their stability during load transients. However, the diagnosis of failures in such elements has barely been studied in the literature. This paper presents a method to diagnose the condition of damper bars in synchronous motors. It is based on the capture of the stator current of the machine during a direct startup and its further analysis in order to track the characteristic transient evolution of a particular fault-related component in the time–frequency map. The fact that the damper only carries significant current during the startup and little or no current, when the machine operates in steady state, makes this transient-based approach specially suited for the detection of such failure. The Hilbert–Huang transform (based on the empirical mode decomposition method) is proposed as a signal-processing tool. Simulation and experimental results on laboratory synchronous machines prove the validity of the approach for condition monitoring of such windings.
    IEEE Transactions on Energy Conversion 01/2012; 27(2):432-439. · 3.35 Impact Factor
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    ABSTRACT: This paper proposes a methodology for the computation of the energy balance and heating curves of an induction motor taking as a basis the information provided by the application of the infrared thermography. Moreover, use of infrared thermography data to diagnose rotor bar failures is preliminarily studied. In the paper, a 1.1 kW induction motor is tested. Thermography images of the chassis temperature are captured at each second during the whole startup transient, from standstill till steady-state, by using an infrared camera connected to a laptop computer fitted with an acquisition and analysis program. A model based on the calculation of the heat losses by convection and radiation through the frame of the machine is developed. It uses the geometrical dimensions as well as temperature values obtained from the thermography images. The developed model enables studying any motor configuration and operating conditions by just varying thermal and geometry parameters. Finally, a preliminary study relying on the possible use of Thermography data for diagnosis of rotor failures is carried out. Experimental data corresponding to a healthy machine and a machine with broken bars are analysed and compared. This methodology will be the baseline for further complex failure diagnosis in electric motors.
    Electrical Machines (ICEM), 2012 XXth International Conference on; 01/2012
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    ABSTRACT: This paper presents three educational test benches especially conceived to illustrate the operation of different induction motors fault diagnosis techniques, both conventional and recently developed approaches. These test benches are used within the context of two laboratory sessions of a subject dealing with predictive maintenance of electrical installations and machinery. During the laboratory sessions, the students must proceed to the integral application of the different condition monitoring techniques. The test benches enable a simple capture of the current signals in machines with rather different constructive characteristics as well as operating and fault conditions. Stationary current signals are later analyzed by using the Fast Fourier Transform (FFT)-conventional approach- whereas the startup current signals are processed using the Discrete Wavelet Transform (DWT)-modern transient-based approach. The obtained results serve to show the students the suitability and application scope of each particular technique with regards to the diagnosis of failures in electric induction motors.
    e-Learning in Industrial Electronics (ICELIE), 2012 6th IEEE International Conference on; 01/2012
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    ABSTRACT: Time–frequency analysis of the transient current in induction motors (IMs) is the basis of the transient motor current signature analysis diagnosis method. IM faults can be accurately identified by detecting the characteristic pattern that each type of fault produces in the time–frequency plane during a speed transient. Diverse transforms have been proposed to generate a 2-D time–frequency representation of the current, such as the short time Fourier transform (FT), the wavelet transform, or the Wigner–Ville distribution. However, a fine tuning of their parameters is needed in order to obtain a high-resolution image of the fault in the time–frequency domain, and they also require a much higher processing effort than traditional diagnosis techniques, such as the FT. The new method proposed in this paper addresses both problems using the Gabor analysis of the current via the chirp z-transform, which can be easily adapted to generate high-resolution time–frequency stamps of different types of faults. In this paper, it is used to diagnose broken bars and mixed eccentricity faults of an IM using the current during a startup transient. This new approach is theoretically introduced and experimentally validated with a 1.1-kW commercial motor in faulty and healthy conditions.
    IEEE Transactions on Instrumentation and Measurement 01/2012; 61(6):1583-1596. · 1.71 Impact Factor
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    ABSTRACT: Fault diagnosis in wind generators is drawing increasing attention among the scientific community and maintenance companies, due to the crucial contribution of this renewable power source in the electric power generation map. These machines usually operate through variable load regimes, a fact that makes the application of most conventional diagnostic techniques not suitable, since they are adapted to analysis of stationary quantities. In this context, the application of modern transient-based methodologies becomes very appropriate. In this paper, a technique based on the application of Wigner-Ville Distribution is proposed to diagnose and quantify rotor asymmetries in induction generators. It relies on the Wigner-Ville analysis of the current produced by these machines while operating under speed varying conditions and on the further tracking of the characteristic evolutions of fault-related components in the resulting time-frequency maps. Preliminary experimental results show the validity of the approach as well as its potential to diagnose this type of failures in wind generators.
    Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), 2012 International Symposium on; 01/2012
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    ABSTRACT: Transient motor current signature analysis is a recently developed technique for motor diagnostics using speed transients. The whole speed range is used to create a unique stamp of each fault harmonic in the time-frequency plane. This greatly increases diagnostic reliability when compared with nontransient analysis, which is based on the detection of fault harmonics at a single speed. But this added functionality comes at a price: well-established signal analysis tools used in the permanent regime, mainly the Fourier transform, cannot be applied to the nonstationary currents of a speed transient. In this paper, a new method is proposed to fill this gap. By applying a polynomial-phase transform to the transient current, a new, stationary signal is generated. This signal contains information regarding the fault components along the different regimes covered by the transient, and can be analyzed using the Fourier transform. The polynomial-phase transform is used in radar, sonar, communications, and power systems fields, but this is the first time, to the best knowledge of the authors, that it has been applied to the diagnosis of induction motor faults. Experimental results obtained with two different commercial motors with broken bars are presented to validate the proposed method.
    IEEE Transactions on Industrial Electronics 05/2011; · 6.50 Impact Factor
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    ABSTRACT: Damper bars play an important role in synchronous motors. Among other functions, they enable the direct-on-line startup of these machines. Moreover, they allow enhancing their stability during load transients. However, the large currents and stresses to which those elements are subjected during their operation intervals increase the probability of winding deterioration and of eventual breakage. This paper presents a logical extension of a transient-based diagnosis approach, whose application was successfully assessed in conventional induction motors. The method is applied here to diagnose the condition of damper bars in synchronous motors. It is based on the capture of the stator startup current of the machine and its further analysis in order to track the characteristic transient evolutions of a particular fault-related harmonic in the time-frequency map. For this purpose, the Hilbert-Huang Transform (HHT), based on the Empirical Mode Decomposition (EMD) method, is used here as proposed signal processing tool, even though other tools may be also viable. Simulations as well as experimental results on a laboratory synchronous machine prove the validity of the approach for condition monitoring of such elements.
    Industrial Electronics (ISIE), 2011 IEEE International Symposium on; 01/2011
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    ABSTRACT: Development of portable devices for reliable condition monitoring of induction machines has become the goal of many researchers. In this context, the development of robust algorithms for the automatic diagnosis of electromechanical failures plays a crucial role. The conventional tool for the diagnostic of most faults is based on the FFT of the steady-state current. However, it implies significant drawbacks in industrial applications in which the machine does not operate under ideal stationary conditions (e.g. presence of pulsating load torques, supply unbalances, noises…). In order to overcome some of these problems, a novel transient-based methodology (Transient Motor Current Signature Analysis, TMCSA) has been recently proposed. The idea is to analyze the current demanded by the machine under transient operation (e.g. during the startup) by using proper Time Frequency Decomposition (TFD) tools in order to identify the presence of specific patterns in the time- frequency map caused by the characteristic evolutions of fault- related components. However, despite the excellent results hitherto obtained, the qualitative identification of the patterns requires a certain user expertness, which implies difficulties for the automation of the diagnosis. A new algorithm for the automatic diagnostic of rotor bar failures is proposed in this paper. It is based on the application of the Hilbert-Huang Transform, sustained on the Empirical Mode Decomposition process, for feature extraction, and the further application of the Scale Transform (ST) for invariant feature selection. The results prove the reliability of the algorithm and its generality to automatically diagnose the fault in machines with rather different sizes and load conditions.
    01/2011;
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    ABSTRACT: In the present study, a methodology has been designed in order to establish an energy balance of an induction motor, through the use of Infrared Thermography. Tests were performed on a 1.1 kW induction motor operating at full load. Thermographic images of the chassis temperature were captured at every second from the starting up of the motor till the switch off and final cool down, by using an infrared camera connected to a laptop computer with an acquisition and analysis program installed. Initially, heat losses by convection and radiation from the surface of the machine were calculated using its geometrical dimensions and temperature values obtained from the thermographic images. Any motor configuration and working conditions can be accurately studied using this procedure by just varying the m·Ce, NTU, μ and/or fan diameter. Additionally, the developed model proves that infrared thermography can be considered as a valuable tool to perform energy balances and to obtain the heating curves of the machine with enough accuracy.
    Industrial Electronics (ISIE), 2011 IEEE International Symposium on; 01/2011
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    ABSTRACT: The present work is focused on the diagnosis of mixed eccentricity faults in induction motors via the study of currents demanded by the machine. Unlike traditional methods, based on the analysis of stationary currents (Motor Current Signature Analysis (MCSA)), this work provides new findings regarding the diagnosis approach proposed by the authors in recent years, which is mainly focused on the fault diagnosis based on the analysis of transient quantities, such as startup or plug stopping currents (Transient Motor Current Signature Analysis (TMCSA)), using suitable time-frequency decomposition (TFD) tools. The main novelty of this work is to prove the usefulness of tracking the transient evolution of high-order eccentricity-related harmonics in order to diagnose the condition of the machine, complementing the information obtained with the low-order components, whose transient evolution was well characterised in previous works. Tracking of high-order eccentricity-related harmonics during the transient, through their associated patterns in the time-frequency plane, may significantly increase the reliability of the diagnosis, since the set of fault-related patterns arising after application of the corresponding TFD tool is very unlikely to be caused by other faults or phenomena. Although there are different TFD tools which could be suitable for the transient extraction of these harmonics, this paper makes use of a Wigner–Ville distribution (WVD)-based algorithm in order to carry out the time-frequency decomposition of the startup current signal, since this is a tool showing an excellent trade-off between frequency resolution at both high and low frequencies. Several simulation results obtained with a finite element-based model and experimental results show the validity of this fault diagnosis approach under several faulty and operating conditions. Also, additional signals corresponding to the coexistence of the eccentricity and other non-fault related phenomena making difficult the diagnosis (fluctuating load torque) are included in the paper. Finally, a comparison with an alternative TFD tool – the discrete wavelet transform (DWT) – applied in previous papers, is also carried out in the contribution. The results are promising regarding the usefulness of the methodology for the reliable diagnosis of eccentricities and for their discrimination against other phenomena.
    Mechanical Systems and Signal Processing 01/2011; 25(2):667-679. · 1.91 Impact Factor
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    ABSTRACT: This paper presents an open, multilevel condition monitoring system for induction motors (IMs). The current, voltage, speed and temperature values of the IM are measured with commercial, industrial equipment (power analyzer, temperature relay), and transmitted to a programmable logic controller (PLC) through a MODBUS industrial network. The PLC program, developed under the IEC-61131-3 standard, performs protection logic and transmits the data to a supervision computer using an OPC (Ole for Process Control) data communications server. An OPC client component has been inserted into a SCADA program, which displays the motor status and stores it to perform trend analysis. The use of open specifications for data communications and PLC software development has allowed the construction of the proposed system using industrial, commercially available components, instead of requiring custom developed equipment. This approach also facilitates its integration into information networks at the enterprise level. Experimental results with a commercial 1.1 three-phase IM show the feasibility of the proposed system.
    01/2011;
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    ABSTRACT: In this paper a recently developed induction motors diagnosis methodology is applied to detect mixed eccentricity in Inverter-Fed Induction Motors (IFIMs). The classic FFT method can not be applied when the stator current captured is not in steady state (which is common in IFIMs). The approach is based on obtaining a 2D time - frequency plot representing the time - frequency evolution of the main components in a stator transient current. The time-frequency maps are generated with high detail using the Analytic Wavelet Transform. Thanks to this, the evolutions of the main Winding Harmonics, Principal Slot Harmonics and Eccentricity Related Harmonics are traced precisely. As a consequence, the time-frequency plane characteristic patterns produced by the Eccentricity Related Harmonics are easily and clearly identified enabling a reliable diagnosis. The methodology capabilities have been shown successfully diagnosing a healthy IFIM and an IFIM with mixed eccentricity. The transients analyzed consist of a startup and a decrease in the assigned frequency.
    Electrical Machines (ICEM), 2010 XIX International Conference on; 10/2010
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    ABSTRACT: Motor current signature analysis (MCSA) is a well-established method for the diagnosis of induction motor faults. It is based on the analysis of the spectral content of a motor current, which is sampled while a motor runs in steady state, to detect the harmonic components that characterize each type of fault. The Fourier transform (FT) plays a prominent role as a tool for identifying these spectral components. Recently, MCSA has also been applied during the transient regime (TMCSA) using the whole transient speed range to create a unique stamp of each harmonic as it evolves in the time-frequency plane. This method greatly enhances the reliability of the diagnostic process compared with the traditional method, which relies on spectral analysis at a single speed. However, the FT cannot be used in this case because the fault harmonics are not stationary signals. This paper proposes the use of the fractional FT (FrFT) instead of the FT to perform TMCSA. This paper also proposes the optimization of the FrFT to generate a spectrum where the frequency-varying fault harmonics appear as single spectral lines and, therefore, facilitate the diagnostic process. A discrete wavelet transform (DWT) is used as a conditioning tool to filter the motor current prior to its processing by the FrFT. Experimental results that are obtained with a 1.1-kW three-phase squirrel-cage induction motor with broken bars are presented to validate the proposed method.
    IEEE Transactions on Instrumentation and Measurement 09/2010; · 1.71 Impact Factor

Publication Stats

303 Citations
38.48 Total Impact Points

Institutions

  • 2006–2014
    • Polytechnical University of Valencia
      • • Institute for Energy Engineering (IIE)
      • • Department of Electrical Engineering
      Valenza, Valencia, Spain
  • 2009–2011
    • University of Valencia
      Valenza, Valencia, Spain
    • AGH University of Science and Technology in Kraków
      Cracovia, Lesser Poland Voivodeship, Poland
  • 2002
    • Universidad Politécnica de Cartagena
      Carthago Nova, Murcia, Spain