Manuel Pineda-Sanchez
Polytechnic University of Valencia
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Publication
Featured researches published by Manuel Pineda-Sanchez.
IEEE Transactions on Industrial Electronics | 2008
Martin Riera-Guasp; Jose A. Antonino-Daviu; Manuel Pineda-Sanchez; Ruben Puche-Panadero; J. Perez-Cruz
In this paper, a general methodology based on the application of discrete wavelet transform (DWT) to the diagnosis of the cage motor condition using transient stator currents is exposed. The approach is based on the identification of characteristic patterns introduced by fault components in the wavelet signals obtained from the DWT of transient stator currents. These patterns enable a reliable detection of the corresponding fault as well as a clear interpretation of the physical phenomenon taking place in the machine. The proposed approach is applied to the detection of rotor asymmetries in two alternative ways, i.e., by using the startup current and by using the current during plugging stopping. Mixed eccentricities are also detected by means of the transient-based methodology. This paper shows how the evolution of other non-fault-related components such as the principal slot harmonic (PSH) can be extracted with the proposed technique. A compilation of experimental cases regarding the application of the methodology to the previous cases is presented. Guidelines for the easy application of the methodology by any user are also provided under a didactic perspective.
IEEE Transactions on Energy Conversion | 2009
Ruben Puche-Panadero; Manuel Pineda-Sanchez; Martin Riera-Guasp; J. Roger-Folch; Elias Hurtado-Perez; J. Perez-Cruz
This paper proposes an online/offline induction motor current signature analysis (MCSA) with advanced signal-and-data-processing algorithms, based on the Hilbert transform. MCSA is a method for motor diagnosis with stator-current signals. Although it is one of the most powerful online methods for diagnosing motor faults, it has some drawbacks that can degrade the performance and accuracy of a motor-diagnosis system. In particular, it is very difficult to detect broken rotor bars when the motor is operating at low slip or under no load, due to fast Fourier transform (FFT) frequency leakage and the small amplitude of the current components related to the fault. Therefore, advanced signal-and-data-processing algorithms are proposed. They consist of a proper sample selection algorithm, a Hilbert transformation of the stator-sampled current, and spectral analysis via FFT of the modulus of the resultant time-dependent vector modulus for achieving MCSA efficiently. Experimental results obtained on a 1.1 kW three-phase squirrel-cage induction motor are discussed.
IEEE Transactions on Industrial Electronics | 2011
Joan Pons-Llinares; Jose A. Antonino-Daviu; Martin Riera-Guasp; Manuel Pineda-Sanchez; Vicente Climente-Alarcon
In this paper, a new induction motor diagnosis methodology is proposed. The approach is based on obtaining a 2-D time-frequency plot representing the time-frequency evolution of the main components in an electrical machine transient current. The identification of characteristic patterns in the time-frequency plane caused by many of the fault-related components enables a reliable machine diagnosis. Unlike other continuous-wavelet-transform-based methods, this work uses frequency B-spline (FBS) wavelets. It is shown that these wavelets enable an efficient filtering in the region neighboring the main frequency, as well as enable a high level of detail in the time-frequency maps. As a consequence, the evolution of the most important current components is precisely traced. These characteristics make it easy to identify the patterns related to the fault components. The technique is applied to the experimental no-load start-up current of motors in a healthy state and with broken bars; the FBS capabilities are revealed. One of the novelties of this paper is the fact that the diagnosis is carried out via the identification not only of the traditional lower sideband harmonic but also of the upper sideband harmonic and four additional fault-related components.
IEEE Transactions on Industry Applications | 2009
Jose A. Antonino-Daviu; Martin Riera-Guasp; Manuel Pineda-Sanchez; Rafael B. Pérez
In this paper, a cutting-edge time-frequency decomposition tool, i.e., the Hilbert-Huang transform (HHT), is applied to the stator startup current to diagnose the presence of rotor asymmetries in induction machines. The objective is to extract the evolution during the startup transient of the left sideband harmonic (LSH) caused by the asymmetry, which constitutes a reliable evidence of the presence of the fault. The validity of the diagnosis methodology is assessed through several tests developed using real experimental signals. Moreover, in this paper, an analytical comparison with an alternative time-frequency decomposition tool, i.e., the discrete wavelet transform (DWT), is carried out. This tool was applied in previous works to the transient extraction of fault-related components, with satisfactory results, even in cases in which the classical Fourier approach does not lead to correct results. The results of the application of the HHT and DWT are analyzed and compared, obtaining novel conclusions about their respective suitability for the transient extraction of asymmetry-related components, as well as the equivalence, with regard to the LSH extraction, between their basic components, namely: 1) intrinsic mode function, for the HHT, and 2) approximation signal for the DWT.
IEEE Transactions on Industrial Electronics | 2009
Manuel Pineda-Sanchez; Martin Riera-Guasp; Jose A. Antonino-Daviu; J. Roger-Folch; J. Perez-Cruz; Ruben Puche-Panadero
In this paper, a new method for detecting the presence of broken rotor bars is presented. The proposed approach is valid for induction machines started at constant frequency and consists of extracting the instantaneous frequency (IF) of the left sideband harmonic (LSH) from the start-up current (LSHst), via the Hilbert transform. It is shown that, in the case of machines with one or several broken bars, the IF of the LSHst exhibits a very characteristic and easy to identify pattern, which is physically justified. This paper also shows that, if the IF of the LSHst is represented against the slip, a universal fault indicator (nondependent neither on the machine characteristics nor on the starting conditions) can be defined. This fault indicator consists of the correlation between the experimental IF of the LSHst and its theoretical evolution. This approach is theoretically introduced and experimentally validated by testing a commercial motor in faulty and healthy conditions, under different operating conditions.
IEEE Transactions on Instrumentation and Measurement | 2010
Manuel Pineda-Sanchez; Martin Riera-Guasp; Jose A. Antonino-Daviu; J. Roger-Folch; J. Perez-Cruz; Ruben Puche-Panadero
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 Energy Conversion | 2010
Martin Riera-Guasp; M.F. Cabanas; Jose A. Antonino-Daviu; Manuel Pineda-Sanchez; Carlos H. Rojas Garcia
Studies of rotor asymmetries in squirrel-cage induction motors have traditionally focused on analyses of the effects of the breakage of adjacent bars on the magnetic field and current spectrum. However, major motor manufacturers have reported cases where damaged bars are randomly distributed around the rotor perimeter of large HV machines. In some of these cases, the motors were being monitored under maintenance programs based on motor current signature analysis (MCSA), and the degree of degradation found in the rotor was much greater than that predicted by analysis of their current spectra. For this reason, a complete study was carried out, comprising a theoretical analysis, as well as simulation and tests, to investigate the influence that the number and location of faulty bars has on the traditional MCSA diagnosis procedure. From the theoretical analysis, based on the application of the fault-current approach and space-vector theory, a very simple method is deduced, which enables the left sideband amplitude to be calculated for any double bar breakage, per unit of the sideband amplitude corresponding to a single breakage. The proposed methodology is generalized for the estimation of the sideband amplitude in the case of multiple bar breakages and validated by simulation using a finite-element-based model, as well as by laboratory tests.
IEEE Transactions on Industrial Electronics | 2014
F. Vedreño-Santos; Martin Riera-Guasp; Humberto Henao; Manuel Pineda-Sanchez; Ruben Puche-Panadero
This paper proposes a methodology to improve the reliability of diagnosis of different types of faults in wound-rotor induction generators which work under variable load conditions, as in wind turbine applications; the method is based on the extraction of the instantaneous frequency (IF) of the fault-related components of stator and rotor currents during speed changes caused by nonstationary functioning. It is shown that, under these conditions, the IF versus slip plots of the fault components are straight lines with a specific slope and y-intercept for each kind of fault. In addition, neither of these patterns are dependent on the machine features or the way that the load changes. The practical methodology of this technique is introduced for diagnosing two different anomalies: stator winding asymmetry and rotor winding asymmetry. The approach is validated by laboratory tests for both types of faults in two different kinds of machines.
IEEE Transactions on Instrumentation and Measurement | 2012
Martin Riera-Guasp; Manuel Pineda-Sanchez; J. Perez-Cruz; Ruben Puche-Panadero; J. Roger-Folch; Jose A. Antonino-Daviu
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.
Expert Systems With Applications | 2012
Dragan Matic; Filip Kulic; Manuel Pineda-Sanchez; Ilija Kamenko
Highlights? System for broken bar detection for wide slip range. ? Detection based on measuring only a motor current. ? No need for mathematical models, classifier is trained on acquired data. ? Detection of broken bar at low slip, where classical broken bar detection classifiers are not applicable. ? Reliable, mobile, and cost effective system that can be successfully applied in real working environment. This paper presents a support vector machine classifier for broken bar detection in electrical induction machine. It is a reliable online method, which has high robustness to load variations and changing operating conditions. The phase current is only physical value to be measured. The steady state current is analyzed for broken bar fault via motor current signature analysis technique based on Hilbert transform. A two dimensional feature space is proposed. The features are: magnitude and frequency of characteristic peak extracted from spectrum of Hilbert transform series of the phase current. For classification task support vector machine is used due to its good robustness and generalization performances. A comparative analysis of linear, Gaussian and quadratic kernel function versus error rate and number of support vectors is done. The proposed classifier successfully detects a broken bar in various operational situations. The proposed method is sufficiently accurate, fast, and robust to load changes, which makes it suitable for use in real-time online applications in industrial drives.