Time-frequency vibration analysis for the detection of motor damages caused by bearing currents

[EN] Motor failure due to bearing currents is an issue that has drawn an increasing industrial interest over recent years. Bearing currents usually appear in motors operated by variable frequency drives (VFD); these drives may lead to common voltage modes which cause currents induced in the motor shaft that are discharged through the bearings. The presence of these currents may lead to the motor bearing failure only few months after system startup. Vibration monitoring is one of the most common ways for detecting bearing damages caused by circulating currents; the evaluation of the amplitudes of well-known characteristic components in the vibration Fourier spectrum that are associated with race, ball or cage defects enables to evaluate the bearing condition and, hence, to identify an eventual damage due to bearing currents. However, the inherent constraints of the Fourier transform may complicate the detection of the progressive bearing degradation; for instance, in some cases, other frequency components may mask or be confused with bearing defect-related while, in other cases, the analysis may not be suitable due to the eventual non-stationary nature of the captured vibration signals. Moreover, the fact that this analysis implies to lose the time-dimension limits the amount of information obtained from this technique. This work proposes the use of time-frequency (T-F) transforms to analyse vibration data in motors affected by bearing currents. The experimental results obtained in real machines show that the vibration analysis via T-F tools may provide significant advantages for the detection of bearing current damages; among other, these techniques enable to visualise the progressive degradation of the bearing while providing an effective discrimination versus other components that are not related with the fault. Moreover, their application is valid regardless of the operation regime of the machine. Both factors confirm the robustness and reliability of these tools that may be an interesting alternative for detecting this type of failure in induction motors.This work was supported in part by IPES (which is a joint research laboratory between the Laboratory Ampere and Safran) and in part by the Spanish 'Ministerio de Economia y Competitividad' (MINECO) and FEDER programme in the framework of the 'Proyectos I+D del Subprograma de Generacion de Conocimiento, Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia' (ref: DPI2014-52842-P)."Prudhom, A.; Antonino-Daviu, J.; Razik, H.; Climente Alarcón, V. (2017). Time-frequency vibration analysis for the detection of motor damages caused by bearing currents. Mechanical Systems and Signal Processing. 84:747-762. https://doi.org/10.1016/j.ymssp.2015.12.008S7477628

Analytical Model of Cage Induction Machine Dedicated to the Study of the Inner Race Bearing Fault

This paper presents a new analytical model for inner bearing raceway defect. The model is based on the presentation of different machine inductances as Fourier series without any kind of reference frame transformation. The proposed approach shows that this model is able to give important features on the state of the motor. Simulation based on spectral analysis of stator current signal using Fast Fourier Transform (FFT) and experimental results are given to shed light on the usefulness of the proposed model

PHM survey: implementation of signal processing methods for monitoring bearings and gearboxes

The reliability and safety of industrial equipments are one of the main objectives of companies to remain competitive in sectors that are more and more exigent in terms of cost and security. Thus, an unexpected shutdown can lead to physical injury as well as economic consequences. This paper aims to show the emergence of the Prognostics and Health Management (PHM) concept in the industry and to describe how it comes to complement the different maintenance strategies. It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring. More specifically, this paper provides a state of the art of existing signal processing techniques that can be used in the PHM strategy. This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes

Magnetic Flux Analysis for the Condition Monitoring of Electric Machines: A Review

[EN] Magnetic flux analysis is a condition monitoring technique that is drawing the interest of many researchers and motor manufacturers. The great enhancements and reduction in the costs and dimensions of the required sensors, the development of advanced signal processing techniques that are suitable for flux data analysis, along with other inherent advantages provided by this technology are relevant aspects that have allowed the proliferation of flux-based techniques. This paper reviews the most recent scientific contributions related to the development and application of flux-based methods for the monitoring of rotating electric machines. Particularly, aspects related to the main sensors used to acquire magnetic flux signals as well as the leading signal processing and classification techniques are commented. The discussion is focused on the diagnosis of different types of faults in the most common rotating electric machines used in industry, namely: squirrel cage induction machines (SCIM), wound rotor induction machines (WRIM), permanent magnet machines (PMM) and wound field synchronous machines (WFSM). A critical insight of the techniques developed in the area is provided and several open challenges are also discussed.This work was supported by the Spanish 'Ministerio de Ciencia Innovación y Universidades' and FEDER program in the framework of the "Proyectos de I+D de Generación de Conocimiento del Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnologico del Sistema de I+D+i, Subprograma Estatal de Generacion de Conocimiento" reference PGC2018-095747-B-I00 and by the Consejo Nacional de Ciencia y Tecnología under CONACyT Scholarship with key code 2019-000037-02NACF. Paper no. TII-20-5308.Zamudio-Ramírez, I.; Osornio-Rios, RA.; Antonino-Daviu, J.; Razik, H.; Romero-Troncoso, RDJ. (2022). Magnetic Flux Analysis for the Condition Monitoring of Electric Machines: A Review. IEEE Transactions on Industrial Informatics. 18(5):2895-2908. https://doi.org/10.1109/TII.2021.30705812895290818

Detection of Winding Asymmetries in Wound-Rotor Induction Motors via Transient Analysis of the External Magnetic Field

© 2020 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] Over recent decades, the detection of faults in induction motors (IMs) has been mainly focused in cage motors due to their extensive use. However, in recent years, wound-rotor motors have received special attention because of their broad use as generators in wind turbine units, as well as in some large power applications in industrial plants. Some classical approaches perform the detection of certain faults based on the fast Fourier transform analysis of the steady state current (motor current signature analysis); they have been lately complemented with new transient time-frequency-based techniques to avoid false alarms. Nonetheless, there is still a need to improve the already existing methods to overcome some of their remaining drawbacks and increase the reliability of the diagnostic. In this regard, emergent technologies are being explored, such as the analysis of stray flux at the vicinity of the motor, which has been proven to be a promising option to diagnose the motor condition. Recently, this technique has been applied to detect broken rotor bar failures and misalignments in cage motors, offering the advantage of being a noninvasive tool with simple implementation and even avoiding some drawbacks of well-established tools. However, the application of these techniques to wound rotor IMs (WRIMs) has not been studied. This article explores the analysis of the external magnetic field under the starting to detect rotor winding asymmetry defects in WRIMs by using advanced signal processing techniques. Moreover, a new fault indicator based on this quantity is introduced, comparing different levels of fault and demonstrating the potential of this technique to quantify and monitor rotor winding asymmetries in WRIMs.This work was supported by the Spanish "Ministerio de Ciencia Innovacion y Universidades" and Fondo Europeo de Desarrollo Regional program in the framework of the "Proyectos de I+D de Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I+D+i, Subprograma Estatal de Generacion de Conocimiento" under Grant PGC2018-095747-B-I00.Zamudio-Ramírez, I.; Antonino Daviu, JA.; Osornio-Rios, RA.; Romero-Troncoso, RDJ.; Razik, H. (2020). Detection of Winding Asymmetries in Wound-Rotor Induction Motors via Transient Analysis of the External Magnetic Field. IEEE Transactions on Industrial Electronics. 67(6):5050-5059. https://doi.org/10.1109/TIE.2019.2931274S5050505967

Automatic diagnosis of electromechanical faults in induction motors based on the transient analysis of the stray flux via MUSIC methods

(c) 2020 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 the induction motor predictive maintenance area, there is a continuous search for new techniques and methods that can provide additional information for a more reliable determination of the motor condition. In this context, the analysis of the stray flux has drawn the interest of many researchers. The simplicity, low cost and potential of this technique makes it attractive for complementing the diagnosis provided by other well-established methods. More specifically, the study of this quantity under the starting has been recently proposed as a valuable tool for the diagnosis of certain electromechanical faults. Despite this fact, the research in this approach is still incipient and the employed signal processing tools must be still optimized for a better visualization of the fault components. Moreover, the development of advanced algorithms that enable the automatic identification of the resulting transient patterns is another crucial target within this area. This article presents an advanced algorithm based on the combined application of MUSIC and neural networks that enables the automatic identification of the time-frequency patterns created by the stray flux fault components under starting as well as the subsequent determination of the fault severity level. Two faults are considered in the work: rotor problems and misalignments. Also, different positions of the external coil sensor are studied. The results prove the potential of the intelligent algorithm for the reliable diagnosis of electromechanical faults.This work was supported in part by the Spanish "Ministerio de Ciencia Innovacion y Universidades" and in part by FEDER program in the "Proyectos de I+D de Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I+D+i, Subprograma Estatal de Generacion de Conocimiento" (PGC2018-095747-B-I00).Zamudio-Ramírez, I.; Ramirez-Núñez, JA.; Antonino Daviu, JA.; Osornio-Rios, RA.; Quijano-Lopez, A.; Razik, H.; Romero-Troncoso, RDJ. (2020). Automatic diagnosis of electromechanical faults in induction motors based on the transient analysis of the stray flux via MUSIC methods. IEEE Transactions on Industry Applications. 56(4):3604-3613. https://doi.org/10.1109/TIA.2020.2988002S3604361356

Représentations Systèmes Multi-Machines (SMM) de machines polyphasées

Cet article présente le principe de décomposition de machines polyphasées en machines fictives monophasée et diphasées non couplées magnétiquement. Après la description de la méthodologie de décomposition SMM (Systèmes Multimachines Multiconvertisseurs), deux cas sont étudiés. Une machine synchrone pentaphasée, est d'abord analysée avec son modèle de machines équivalentes. Un second cas plus original est ensuite étudié : deux machines pentaphasées connectées en série et alimentées par un onduleur 5 bras.This paper presents the equivalence of multi-phase machines with a set a of 1-phase and 2-phase machines with no magnetic couplings. Two cases are then studied. First, a 5-phase machine supplied by a Voltage Source Inverter(VSI) is analyzed. Then, a model is established for a single 5-leg VSI supplying two 5-phase machines whose windings are connected in series

Handbook of asynchronous machine with variable speed

ISBN-13: 978-1-84821-225-1 http://www.wiley.com/WileyCDA/WileyTitle/productCd-1848212259.htmlThis handbook deals with the asynchronous machine in its close environment. It was born from a reflection on this electromagnetic converter whose integration in industrial environments takes a wide part. Previously this type of motor operated at fixed speed, from now on it has been integrated more and more in processes at variable speed. For this reason it seemed useful, or necessary, to write a handbook on the various aspects from the motor in itself, via the control and while finishing by the diagnosis aspect. Indeed, an asynchronous motor is used nowadays in industry where variation speed and reliability are necessary. We must know permanently for the sensitive systems, the state of process and to inform the operator of the appearance of any anomaly and its severity

Modélisation et diagnostic de la machine asynchrone en présence de défaillances

Dans cette étude, nous abordons le diagnostic des défauts rotoriques dans les machines asynchrones à cage d'écureuil. Après avoir décrit les différents éléments de constitution d'une machine asynchrone, nous proposons un modèle de machine basée sur la méthode des circuits électriques magnétiquement couplés. Nous présentons ensuite trois méthodes permettant la détection d'un défaut rotorique. La première méthode s'appuie sur l'évaluation de plusieurs indices calculés à partir de l'amplitude des composantes présentes dans les spectres de la puissance instantanée et du courant absorbé par le moteur. La seconde méthode de détection proposée utilise la phase du spectre du courant statorique calculée à partir d'une transformée de Fourier. Nous utilisons ensuite la phase de la transformée de Hilbert calculée à partir du module du spectre du courant statorique. Ces approches ont la particularité de ne se baser sur aucun seuil de référence pour diagnostiquer un défaut rotorique.In this study, we move on to the broken rotor bar diagnosis of squirrel-cage induction machines. The first part is devoted to the development of a model which is based on the magnetically coupled electric circuits. We present three methods allowing detection of a rotor defect of an induction machine. The first method is based on the evaluation of several indexes calculated starting from the amplitude of the components present in the spectra of the instantaneous power and the line current. The second method of detection suggested uses the stator current spectrum phase calculated starting from a Fourier Transform. To improve the detection, we use the Hilbert transform phase calculated starting from the stator current spectrum module. These approaches have the characteristic to be based on any threshold of reference to establish the presence of a broken rotor bar.NANCY1-SCD Sciences & Techniques (545782101) / SudocSudocFranceF

Diagnostic Methods for the Health Monitoring of Gearboxes

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