Quantitative Phase Field Model of Alloy Solidification

We present a detailed derivation and thin interface analysis of a phase-field model that can accurately simulate microstructural pattern formation for low-speed directional solidification of a dilute binary alloy. This advance with respect to previous phase-field models is achieved by the addition of a phenomenological "antitrapping" solute current in the mass conservation relation [A. Karma, Phys. Rev. Lett 87, 115701 (2001)]. This antitrapping current counterbalances the physical, albeit artificially large, solute trapping effect generated when a mesoscopic interface thickness is used to simulate the interface evolution on experimental length and time scales. Furthermore, it provides additional freedom in the model to suppress other spurious effects that scale with this thickness when the diffusivity is unequal in solid and liquid [R. F. Almgren, SIAM J. Appl. Math 59, 2086 (1999)], which include surface diffusion and a curvature correction to the Stefan condition. This freedom can also be exploited to make the kinetic undercooling of the interface arbitrarily small even for mesoscopic values of both the interface thickness and the phase-field relaxation time, as for the solidification of pure melts [A. Karma and W.-J. Rappel, Phys. Rev. E 53, R3017 (1996)]. The performance of the model is demonstrated by calculating accurately for the first time within a phase-field approach the Mullins-Sekerka stability spectrum of a planar interface and nonlinear cellular shapes for realistic alloy parameters and growth conditions.Comment: 51 pages RevTeX, 5 figures; expanded introduction and discussion; one table and one reference added; various small correction

Validation of a New Method for the Diagnosis of Rotor bar Failures via Wavelet Transformation in Industrial Induction Machines

[EN] In this paper, the authors propose a method for the diagnosis of rotor bar failures in induction machines, based on the analysis of the stator current during the startup using the discrete wavelet transform (DWT). Unlike other approaches, the study of the high-order wavelet signals resulting from the decomposition is the core of the proposed method. After an introduction of the physical and mathematical bases of the method, a description of the proposed approach is given; for this purpose, a numerical model of induction machine is used in such a way that the effects of a bar breakage can clearly be shown, avoiding the influence of other phenomena not related with the fault. Afterward, the new diagnosis method is validated using a set of commercial induction motors. Several experiments are developed under different machine conditions (healthy machine and machine with different levels of failure) and operating conditions (no load, full load, pulsating load, and fluctuating voltage). In each case, the results are compared with those obtained using the classical approach, based on the analysis of the steady-state current using the Fourier transform. Finally, the results are discussed, and some considerations about the influence of the DWT parameters (type of mother wavelet, order of the mother wavelet, sampling rate, or number of levels of the decomposition) over the diagnosis are done.Antonino-Daviu, J.; Riera-Guasp, M.; Roger-Folch, J.; Molina Palomares, MP. (2006). Validation of a New Method for the Diagnosis of Rotor bar Failures via Wavelet Transformation in Industrial Induction Machines. IEEE Transactions on Industry Applications. 42(4):990-996. doi:10.1109/TIA.2006.876082S99099642

The Use of the Wavelet Approximation Signal as a Tool for the Diagnosis of Rotor Bar Failures

(c) 2008 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] The aim of this paper is to present a new approach for rotor bar failure diagnosis in induction machines. The method focuses on the study of an approximation signal resulting from the wavelet decomposition of the startup stator current. The presence of the left sideband harmonic is used as evidence of the rotor failure in most diagnosis methods based on the analysis of the stator current. Thus, a detailed description of the evolution of the left sideband harmonic during the startup transient is given in this paper; for this purpose, a method for calculating the evolution of the left sideband during the startup is developed, and its results are physically explained. This paper also shows that the approximation signal of a particular level, which is obtained from the discrete wavelet transform of the startup stator current, practically reproduces the time evolution of the left sideband harmonic during the startup. The diagnosis method proposed here consists of checking if the selected approximation signal fits well the characteristic shape of the left sideband harmonic evolution described in this paper. The method is validated through laboratory tests. The results prove that it can constitute a useful tool for the diagnosis of rotor bar breakages.Riera-Guasp, M.; Antonino-Daviu, J.; Roger-Folch, J.; Molina Palomares, MP. (2008). The Use of the Wavelet Approximation Signal as a Tool for the Diagnosis of Rotor Bar Failures. IEEE Transactions on Industry Applications. 44(3):716-726. doi:10.1109/TIA.2008.921432S71672644

Application and Optimization of the Discrete Wavelet Transform for the Detection of Broken Rotor Bars in Induction Machines

[EN] The problem of the bar breakage diagnosis in electrical induction cage machines is a matter of increasing concern nowadays, due to the widely spread use of these machines in the industry. The classical approach, focused on the Fourier analysis of the steady-state current, has some drawbacks that could be avoided if a study of the transient behavior of the machine is performed. The discrete wavelet transform (DWT) is an ideal tool for this purpose, due to its suitability for the analysis of signals whose frequency spectrum is variable in time. The paper shows how the study of the high-level signals resulting from the DWT of the transient starting current of an induction motor allows the detection of a particular characteristic harmonic that occurs when a rotor bar breakage has taken place. This constitutes an alternative approach that avoids some problems that the traditional method implies and that can even lead to a wrong diagnosis of the fault. In the work, the application of the DWT for broken bar detection is optimized, regarding certain parameters of the transform such as type of the mother wavelet, number of decomposition levels, order of the mother wavelet and sampling frequency. (C) 2006 Elsevier Inc. All rights reserved.Antonino-Daviu, J.; Riera-Guasp, M.; Roger-Folch, J.; Martínez Jiménez, F.; Peris Manguillot, A. (2006). Application and Optimization of the Discrete Wavelet Transform for the Detection of Broken Rotor Bars in Induction Machines. Applied and Computational Harmonic Analysis. 21(2):268-279. doi:10.1016/j.acha.2005.12.003S26827921

Toward condition monitoring of damper windings in synchronous motors via EMD analysis

(c) 2012 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] 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. © 2012 IEEE.This work was supported by the Spanish Ministerio de Ciencia e Innovacion (MICINN) in the framework of the VI Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica 2008-2011. (Programa Nacional de proyectos de Investigacion Fundamental, project reference DPI2011-23740). Paper no. TEC-00443-2011.Antonino-Daviu, J.; Riera-Guasp, M.; Pons Llinares, J.; Roger-Folch, J.; Perez, R.; Charlton-Perez, C. (2012). Toward condition monitoring of damper windings in synchronous motors via EMD analysis. IEEE Transactions on Energy Conversion. 27(2):432-439. https://doi.org/10.1109/TEC.2012.2190292S43243927

Diagnosis of Induction Motor Faults in Time-Varying Conditions Using the Polynomial-Phase Transform of the Current

© 2011 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] 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 non-transient 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.This work was supported by the Spanish "Ministerio de Educacion y Ciencia" in the framework of the "Programa Nacional de Proyectos de Investigacion Fundamental," project reference DPI2008-06583/DPI.Pineda-Sanchez, M.; Riera-Guasp, M.; Roger-Folch, J.; Antonino-Daviu, J.; Pérez-Cruz, J.; Puche-Panadero, R. (2011). Diagnosis of Induction Motor Faults in Time-Varying Conditions Using the Polynomial-Phase Transform of the Current. IEEE Transactions on Industrial Electronics. 58(4):1428-1439. https://doi.org/10.1109/TIE.2010.2050755S1428143958

Diagnosis of induction motor faults via gabor analysis of the current in transient regime

© 2011 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] 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. © 2012 IEEE.This work was supported by the Spanish Ministerio de Ciencia e Innovacion (MICINN) in the framework of the VI Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica 2008-2011. (Programa Nacional de proyectos de Investigacion Fundamental, project reference DPI2011-23740). The Associate Editor coordinating the review process for this paper was Dr. Subhas Mukhopadhyay.Riera-Guasp, M.; Pineda-Sanchez, M.; Pérez-Cruz, J.; Puche-Panadero, R.; Roger-Folch, J.; Antonino-Daviu, J. (2012). Diagnosis of induction motor faults via gabor analysis of the current in transient regime. IEEE Transactions on Instrumentation and Measurement. 61(6):1583-1596. doi:10.1109/TIM.2012.2186650S1583159661

Diagnosis of Induction Motor Faults in the Fractional Fourier Domain

[EN] 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.This work was supported by the European Community's Seventh Framework Program FP7/2007-2013 under Grant Agreement 224233 (Research Project PRODI "Power Plant Robustification Based on Fault Detection and Isolation Algorithms"). The Associate Editor coordinating the review process for this paper was Dr. Subhas Mukhopadhyay.Pineda-Sanchez, M.; Riera-Guasp, M.; Antonino-Daviu, J.; Roger-Folch, J.; Perez-Cruz, J.; Puche-Panadero, R. (2010). Diagnosis of Induction Motor Faults in the Fractional Fourier Domain. IEEE Transactions on Instrumentation and Measurement. 59(8):2065-2075. https://doi.org/10.1109/TIM.2009.2031835S2065207559

Transient tracking of low and high-order eccentricity-related components in induction motors via TFD tools

[EN] 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 WignerVille 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. © 2010 Elsevier Ltd.All rights reserved.This work was supported by the Spanish 'Ministerio de Educacion y Ciencia', in the framework of the 'Programa Nacional de proyectos de Investigacion Fundamental', project reference DP12008-06583/DPI.Climente Alarcón, V.; Antonino-Daviu, J.; Riera-Guasp, M.; Pons Llinares, J.; Roger-Folch, J.; Jover-Rodriguez, P.; Arkkio, A. (2011). Transient tracking of low and high-order eccentricity-related components in induction motors via TFD tools. Mechanical Systems and Signal Processing. 25(2):667-679. https://doi.org/10.1016/j.ymssp.2010.08.008S66767925

Calculation of Winding Inductances via Magnetic Vector Potential, Discrete Convolution and Fast Fourier Transform

[Otro] Przedstawiono analiz¿ uszkodze¿ silnika indukcyjnego spowodowanych z¿amaniem pr¿tu, lub zwarciem wewn¿trznym. Oszacowano amplitud¿ wynikaj¿c¿ z uszkodzenia. Induktancja uzwoje¿ jest kluczowa w tym modelu. Zaproponowana metoda jest bardzo prosta i szybka. Zastosowano wektorowy potencja¿ magnetyczny oraz szybk¿ transformat¿ Fouriera.[EN] Fault analysis of induction motors with broken bars or inter-turn short circuits needs accurate machine models to correctly identify and quantify the magnitude of the fault. Winding inductances and their derivatives are a key component of these models. In this paper a new, very easy and extremely fast method to compute them, based on the Magnetic Vector Potential (MVP), the Discrete Circular Convolution and the Fast Fourier Transform (FFT), is presented.This work was supported by the European Community's Seventh Framework Program FP7/2007-2013 under Grant Agreement 224233 (Research Project PRODI "Power Plant Robustification based on Fault Detection and Isolation Algorithms")Pineda-Sánchez, M.; Roger-Folch, J.; Pérez-Cruz, J.; Riera-Guasp, M.; Puche-Panadero, R.; Antonino-Daviu, J.; Pons Llinares, J. (2010). Calculation of Winding Inductances via Magnetic Vector Potential, Discrete Convolution and Fast Fourier Transform. Przegląd Elektrotechniczny. 86(5):109-113. http://hdl.handle.net/10251/103509S10911386