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

A critical comparison between DWT and Hilbert-Huang-based methods for the diagnosis of rotor bar failures in induction machines

(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] 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.This work was supported in part by the Spanish “Ministerio de Educación y Ciencia,” in the framework of the “Programa Nacional de proyectos de Investigación Fundamental,” project reference DPI2008-06583/DPI and in part by “Vicerrectorado de Investigación, Desarrollo e Innovación of the Universidad Politécnica de Valencia” through the Programa de Apoyo a la Investigación y Desarrollo under Contract PAID-06-07.Antonino-Daviu, J.; Riera-Guasp, M.; Pineda-Sanchez, M.; Pérez, RB. (2009). A critical comparison between DWT and Hilbert-Huang-based methods for the diagnosis of rotor bar failures in induction machines. IEEE Transactions on Industry Applications. 45(5):1794-1803. https://doi.org/10.1109/TIA.2009.2027558S1794180345

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

Enhanced Simulink Induction Motor Model for Education and Maintenance Training

[EN] The training of technicians in maintenance requires the use of signals produced by faulty machines in different operating conditions, which are difficult to obtain either from the industry or through destructive testing. Some tasks in electricity and control courses can also be complemented by an interactive induction machine model having a wider internal parameter configuration. This paper presents a new analytical model of induction machine under fault, which is able to simulate induction machines with rotor asymmetries and eccentricity in different load conditions, both stationary and transient states and yielding magnitudes such as currents, speed and torque. This model is faster computationally than the traditional method of simulating induction machine faults based on the Finite Element Method and also than other analytical models due to the rapid calculation of the inductances. The model is presented in Simulink by Matlab for the comprehension and interactivity with the students or lecturers and also to allow the easy combination of the effect of the fault with external influences, studying their consequences on a determined load or control system. An associated diagnosis tool is also presented.This work was supported by the Spanish Ministerio de Ciencia e Innovación under the framework of the Programa Nacional de Proyectos de Investigación Fundamental, Project Reference DPI2011-23740Pineda-Sanchez, M.; Climente Alarcón, V.; Riera-Guasp, M.; Puche-Panadero, R.; Pons Llinares, J. (2012). Enhanced Simulink Induction Motor Model for Education and Maintenance Training. Journal of Systemics, Cybernetics and Informatics. 10(2):92-97. http://hdl.handle.net/10251/105282S929710

Transient-Based Rotor Cage Assessment in Induction Motors Operating With Soft Starters

[EN] The reliable assessment of the rotor condition in induction motors is a matter of increasing concern in the industry. Although rotor damages are more likely in line-started motors operating under high inertias, some cases of broken rotor bars in motors supplied via soft starters have been also reported in the industry. Motor current signature analysis (MCSA) is the most widely spread approach to diagnose such failures. However, its serious drawbacks in many real industrial applications have encouraged investigation on alternative methods enhancing the reliability of the diagnosis. This paper extends a recently introduced diagnosis methodology relying on the startup current analysis to the case of soft-starter-operated motors. The approach has proven to provide very satisfactory results, even in cases where the classical MCSA does not lead to correct diagnosis conclusions. However, its extension to operation under soft starters was still a pending issue. The experimental results shown in this paper ratify the validity of the proposed diagnosis approach in soft-starteroperated induction motors.This work was supported by the Spanish “Ministerio de Economía y Competitividad” (MINECO) in the framework of the “Proyectos I+D del Subprograma de Generación de Conocimiento, Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia” under Grant DPI2014-52842-PCorral Hernández, JÁ.; Antonino-Daviu, J.; Pons Llinares, J.; Climente Alarcón, V.; Francés-Galiana, V. (2015). Transient-Based Rotor Cage Assessment in Induction Motors Operating With Soft Starters. IEEE Transactions on Industry Applications. 51(5):3734-3742. https://doi.org/10.1109/TIA.2015.2427271S3734374251

Particle filter-based estimation of instantaneous frequency for the diagnosis of electrical asymmetries in induction machines

"© 2014 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.” Upon publication, authors are asked to include either a link to the abstract of the published article in IEEE Xplore®, or the article’s Digital Object Identifier (DOI).Fault diagnosis of induction machines operating under variable load conditions is still an unsolved matter. Under those regimes, the application of conventional diagnostic techniques is not suitable, since they are adapted to the analysis of stationary quantities. In this context, modern transient-based methodologies become very appropriate. This paper improves a technique, based on the application of Wigner Ville distribution as time frequency decomposition tool, using a particle filtering method as feature extraction procedure, to diagnose and quantify electrical asymmetries in induction machines, such as wound- rotor induction generators used in wind farms. The combination of both tools allows tracking several variable frequency harmon- ics simultaneously and computing their energy with high accu- racy, yielding magnitudes and values similar to those obtained by the application of the fast Fourier transform in stationary operation. The experimental results show the validity of the approach for rapid speed variations, independently of any speed sensor.Climente Alarcon, V.; Antonino Daviu, JA.; Haavisto, A.; Arkkio, A. (2014). Particle Filter-Based Estimation of Instantaneous Frequency for the Diagnosis of Electrical Asymmetries in Induction Machines. IEEE Transactions on Instrumentation and Measurement. 63(10):2454-2463. doi:10.1109/TIM.2014.231011324542463631

Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

© 2015 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] Recently, research concerning electrical machines and drives condition monitoring and fault diagnosis has experienced extraordinarily dynamic activity. The increasing importance of these energy conversion devices and their widespread use in uncountable applications have motivated significant research efforts. This paper presents an analysis of the state of the art in this field. The analyzed contributions were published in most relevant journals and magazines or presented in either specific conferences in the area or more broadly scoped events.Riera-Guasp, M.; Antonino-Daviu, J.; Capolino, G. (2015). Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art. IEEE Transactions on Industrial Electronics. 62(3):1746-1759. doi:10.1109/TIE.2014.2375853S1746175962

Modeling and fault diagnosis of broken rotor bar faults in induction motors

Due to vast industrial applications, induction motors are often referred to as the “workhorse” of the industry. To detect incipient faults and improve reliability, condition monitoring and fault diagnosis of induction motors are very important. In this thesis, the focus is to model and detect broken rotor bar (BRB) faults in induction motors through the finite element analysis and machine learning approach. The most successfully deployed method for the BRB fault detection is Motor Current Signature Analysis (MSCA) due to its non-invasive, easy to implement, lower cost, reliable and effective nature. However, MSCA has its own limitations. To overcome such limitations, fault diagnosis using machine learning attracts more research interests lately. Feature selection is an important part of machine learning techniques. The main contributions of the thesis include: 1) model a healthy motor and a motor with different number of BRBs using finite element analysis software ANSYS; 2) analyze BRB faults of induction motors using various spectral analysis algorithms (parametric and non-parametric) by processing stator current signals obtained from the finite element analysis; 3) conduct feature selection and classification of BRB faults using support vector machine (SVM) and artificial neural network (ANN); 4) analyze neighbouring and spaced BRB faults using Burg and Welch PSD analysis

Automatic Pattern Identification Based on the Complex Empirical Mode Decomposition of the Startup Current for the Diagnosis of Rotor Asymmetries in Asynchronous Machines

[EN] This paper presents an advanced signal processing method applied to the diagnosis of rotor asymmetries in asynchronous machines. The approach is based on the application of complex empirical mode decomposition to the measured start-up current and on the subsequent extraction of a specific complex intrinsic mode function. Unlike other approaches, the method includes a pattern recognition stage that makes possible the automatic identification of the signature caused by the fault. This automatic detection is achieved by using a reliable methodology based on hidden Markov models. Both experimental data and a hybrid simulation experimental approach demonstrate the effectiveness of the proposed methodology.This work was supported in part by the Conselleria d'Educacio, Formacio i Ocupacio of the Generalitat Valenciana, in the framework of the "Ayudas para la Realizacion de Proyectos de I+D para Grupos de Investigacion Emergentes," project reference GV/2012/020.Georgoulas, G.; Tsoumas, IP.; Antonino-Daviu, J.; Climente Alarcón, V.; Stylios, CD.; Mitronikas, ED.; Safacas, AN. (2014). Automatic Pattern Identification Based on the Complex Empirical Mode Decomposition of the Startup Current for the Diagnosis of Rotor Asymmetries in Asynchronous Machines. IEEE Transactions on Industrial Electronics. 61(9):4937-4946. https://doi.org/10.1109/TIE.2013.22841434937494661

Performance of Induction Machines

Induction machines are one of the most important technical applications for both the industrial world and private use. Since their invention (achievements of Galileo Ferraris, Nikola Tesla, and Michal Doliwo-Dobrowolski), they have been widely used in different electrical drives and as generators, thanks to their features such as reliability, durability, low price, high efficiency, and resistance to failure. The methods for designing and using induction machines are similar to the methods used in other electric machines but have their own specificity. Many issues discussed here are based on the fundamental achievements of authors such as Nasar, Boldea, Yamamura, Tegopoulos, and Kriezis, who laid the foundations for the development of induction machines, which are still relevant today. The control algorithms are based on the achievements of Blaschke (field vector-oriented control) and Depenbrock or Takahashi (direct torque control), who created standards for the control of induction machines. Today’s induction machines must meet very stringent requirements of reliability, high efficiency, and performance. Thanks to the application of highly efficient numerical algorithms, it is possible to design induction machines faster and at a lower cost. At the same time, progress in materials science and technology enables the development of new machine topologies. The main objective of this book is to contribute to the development of induction machines in all areas of their applications