J. Roger-Folch

Polytechnical University of Valencia, Valenza, Valencia, Spain

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Publications (59)46.73 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: The diagnosis of induction machines using Fourier transform relies on tracking the frequency signature of each type of fault in the current's spectrum, but this signature depends on the machine's slip and the supply frequency, so it must be recomputed for each working condition by trained personnel or by diagnostic software. Besides, sampling the current at high rates during long times is needed to achieve a good spectral resolution, which requires large memory space to store and process the current spectra. In this paper, a novel approach is proposed to solve both problems. It is based on the fact that each type of fault generates a series of harmonics in the current's spectrum, whose frequencies are multiples of a characteristic main fault frequency. The tracking analysis of the fault components using the harmonic order (defined as the frequency in per unit of the main fault frequency) as independent variable instead of the frequency generates a unique fault signature, which is the same for any working condition. Besides, this signature can be concentrated in just a very small set of values, the amplitudes of the components with integer harmonic order. This new approach is introduced theoretically and validated experimentally.
    No preview · Article · Sep 2015 · IEEE Transactions on Energy Conversion
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    ABSTRACT: Most of automation systems in industry are based on Programmable Logical Controllers (PLCs) due to its reliability and immunity to industrial environments. Besides, PLCs's flexibility and capabilities have allowed human to automate a huge number of industrial processes. In this paper a new multilevel graphical and modular approach is proposed for programming industrial PLCss. Traditional programming drawbacks, such as impossibility of exchange code or the programmer dependence, are well-known. For solve these problems, the paradigm proposed in this paper exploits several object oriented features, improving the re-use of code, develops objects to be used in a plug and play way understandable by so many developers. Hence this framework reduce the PLC brand and programmer dependence.
    No preview · Article · Jun 2015
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    ABSTRACT: Improved fault diagnostic techniques in wind turbines is a field of growing interest, given the negative impact that unexpected breakdowns have on the profitability of wind farms. New diagnostic techniques based on generator currents monitoring have recently been developed, but their use is still irrelevant despite the advantages that current monitoring presents versus monitoring vibrations. In part, this situation can be due to the needs of relatively high computing power not available in the wind-groups and also, to the use of signals that generate volumes of data difficult to transfer to control centers, where they could be processed. This paper introduces a methodology that aims to solve these problems. In this paper a novel diagnostic method based on monitoring the generator currents is proposed. This approach is based on the tracking analysis of the fault components using the harmonic order as independent variable. This approach can be implemented in low cost digital devices; the resultant patterns are very simple, since their shape is the same no matter the changes in the fundamental frequency and thus, are easily interpretable even by non-qualified persons. Moreover these patterns are characterized by a very low number of parameters, which make easy their transmission to remote control centers. This new approach is theoretically justified and validated by laboratory tests.
    No preview · Article · Nov 2014
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    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. © 2014. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
    Full-text · Article · Oct 2014 · Mechanical Systems and Signal Processing
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    ABSTRACT: Undergraduate courses in electrical machines often include an introduction to their magnetic circuits and to the various magnetic materials used in their construction and their properties. The students must learn to be able to recognize and compare the permeability, saturation, and losses of these magnetic materials, relate each material to its specific properties, and understand the impact of these properties on the major performance metrics of electrical machines. This paper describes a new test equipment setup and lab guide that helps students achieve these learning goals. The test equipment consists of two transformers of grain-oriented and non-grain-oriented electrical steel, transducers, a data acquisition (DAQ) board and a PC-based virtual instrument. The virtual instrument shows voltage, current, and core flux time waveforms, the rms voltage versus current curves and, most importantly, the lamination material magnetic cycle. Students' laboratory work was organized into a series of experiments that guide their achievement of these magnetic materials-related abilities. Pre- and post-lab exams assessed student learning, which was shown to have increased significantly. Students' opinions of the relevance, usefulness, and motivational effect of the laboratory were also positive.
    No preview · Article · Sep 2014 · IEEE Transactions on Education
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    ABSTRACT: The diagnosis of electrical motors through the detection of fault frequency signatures in the current's spectrum has become an established standard in the field of industrial maintenance systems. Nevertheles, its implementation on devices with low computing power remains a practical challenge. Industrial controllers, such as programmable logic controllers, or modern, low cost controller hardware, such as the Arduino or the Raspberry Pi open source hardware proposals, lack both the on-board memory and the high speed data acquisition hardware to perform an accurate spectral analysis of the machine's current, in order to identify the spectral components produced by each type of fault. In this paper, a signal conditioning unit, based on a novel downsampling method of the current, is presented. This unit reduces the amount of current samples that must be processed by the diagnostic unit to a mere sample per current cycle, maintains the sub-hertz accuracy needed to resolve fault, and converts the mains component into a constant value that can be easily eliminated without using any additional filter. Besides, it is implemented using low cost devices, just resistors and operational amplifiers. The proposed method is theoretically developed in this paper, and it has been validated using induction motors with broken bars fed directly by the mains or through variable speed drives.
    No preview · Conference Paper · Sep 2014
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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.
    No preview · Conference Paper · May 2014
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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.
    No preview · Article · Dec 2013 · Engineering Failure Analysis
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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.
    No preview · Article · Dec 2013 · IEEE Transactions on Energy Conversion
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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.
    No preview · Conference Paper · Aug 2013
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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.
    No preview · Conference Paper · Jan 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.
    No preview · Conference Paper · Jan 2013
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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.
    No preview · Conference Paper · Oct 2012
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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.
    No preview · Article · Jun 2012 · IEEE Transactions on Energy Conversion
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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.
    No preview · Conference Paper · Jun 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.
    No preview · Article · Jun 2012 · IEEE Transactions on Instrumentation and Measurement
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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.
    No preview · Conference Paper · Jan 2012
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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.
    No preview · Article · Sep 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.
    No preview · Conference Paper · Jun 2011
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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.
    No preview · Article · May 2011 · IEEE Transactions on Industrial Electronics

Publication Stats

735 Citations
46.73 Total Impact Points


  • 1995-2014
    • Polytechnical University of Valencia
      • • Institute for Energy Engineering (IIE)
      • • Department of Electrical Engineering
      Valenza, Valencia, Spain
  • 2007-2012
    • University of Valencia
      Valenza, Valencia, Spain
    • University of A Coruña
      La Corogne, Galicia, Spain
  • 2002
    • Universidad Politécnica de Cartagena
      • Systems and Automatic Engineering
      Carthago Nova, Murcia, Spain
  • 1998
    • Universidad Politécnica de Madrid
      Madrid, Madrid, Spain