Diagnóstico de fallos electromecánicos en motores eléctricos mediante el análisis avanzado del flujo magnético y su implementación en hardware

[ES] Los motores eléctricos son máquinas eléctricas rotatorias que permiten realizar la conversión de energía eléctrica en energía mecánica, misma que resulta de gran utilidad en diversos procesos industriales, principalmente para accionar mecanismos y cadenas cinemáticas complejas que ejecutan alguna tarea en específico. Dentro de los distintos tipos de motores eléctricos, las máquinas de inducción se han utilizado ampliamente en una gran variedad de procesos industriales. Esto se debe principalmente a sus excelentes características y prestaciones, como lo son: robustez, fácil control, simplicidad, confiabilidad, y fácil mantenimiento. Sin embargo, a pesar de su elevada robustez, este tipo de máquinas se encuentran sujetas a esfuerzos mecánicos, térmicos, eléctricos y ambientales bajo diversas condiciones de operación durante su vida útil, lo que de forma inevitable conduce a fallos. Los principales fallos que suelen presentarse en los motores eléctricos de inducción son aquellos relacionados a sus componentes internos como lo son barras de rotor rotas, desgaste en las pistas exterior e interior en los rodamientos, desgaste en la jaula del rodamiento, fallas en el estator (por ejemplo, cortocircuito entre espiras adyacentes), entre otros. Cuando un motor eléctrico se encuentra operando bajo alguna condición de falla su rendimiento puede verse afectado, lo que se traduce en consumos de energía más elevados, causando a su vez costos extras al momento de facturar. En este trabajo de investigación se presenta el desarrollo de una metodología innovadora, capaz de generar un diagnóstico adecuado y de forma automática de la ocurrencia de las fallas más comunes que pueden desarrollarse en los motores eléctricos de inducción bajo diversas condiciones de operación mediante el análisis del flujo magnético de dispersión (que puede ser capturado en la periferia del marco del motor) empleando herramientas de descomposición en tiempo-frecuencia, herramientas de clasificación de datos y parámetros de caracterización de señales. Así entonces, se prueba la validez de técnicas basadas en el análisis del flujo magnético de dispersión para el diagnóstico de distintas fallas electromecánicas en motores de inducción. Los resultados demuestran el excelente desempeño de la metodología de diagnóstico automático propuesta, al ser evaluada en una gran variedad de motores con diversas características constructivas.[CA] Els motors elèctrics són màquines elèctriques rotatòries que permeten realitzar la conversió d'energia elèctrica en energia mecànica, la qual cosa resulta de gran utilitat en diversos processos industrials, principalment per a accionar mecanismes i cadenes cinemàtiques complexes que executen alguna tasca en específic. Dins dels diferents tipus de motors elèctrics, les màquines d'inducció s'han utilitzat àmpliament en una gran varietat de processos industrials. Això es deu principalment a les seues excel·lents característiques i prestacions, com ara robustesa, fàcil control, simplicitat, fiabilitat i fàcil manteniment. No obstant això, malgrat la seua elevada robustesa, aquest tipus de màquines estan sotmeses a esforços mecànics, tèrmics, elèctrics i ambientals en diverses condicions d'operació durant la seua vida útil, la qual cosa inevitablement condueix a fallades. Les principals fallades que solen presentar-se en els motors elèctrics d'inducció són aquelles relacionades amb els seus components interns, com ara barres de rotor trencades, desgast en les pistes exterior i interior dels rodaments, desgast en la gàbia del rodaments, fallades en l'estator (per exemple, curtcircuit entre espires adjacents), entre altres. Quan un motor elèctric està funcionant sota alguna condició de fallada, el seu rendiment pot veure's afectat, la qual cosa es tradueix en consums d'energia més elevats, causant al seu torn costos addicionals en el moment de facturar. En aquest treball d'investigació es presenta el desenvolupament d'una metodologia innovadora, capaç de generar un diagnòstic adequat i de forma automàtica de l'ocurrència de les fallades més comunes que poden desenvolupar-se en els motors elèctrics d'inducció sota diverses condicions d'operació mitjançant l'anàlisi del flux magnètic de dispersió (que pot ser capturat en la perifèria del marc del motor) emprant eines de descomposició en temps-freqüència, eines de classificació de dades i paràmetres de caracterització de senyals. Així doncs, es prova la validesa de tècniques basades en l'anàlisi del flux magnètic de dispersió per al diagnòstic de diferents fallades electromecàniques en motors d'inducció. Els resultats demostren l'excellent rendiment de la metodologia de diagnòstic automàtic proposada, en ser avaluada en una gran varietat de motors amb diverses característiques constructives.[EN] Electric motors are rotating electrical machines that allow the conversion of electrical energy into mechanical energy, which is very useful in various industrial processes, mainly to drive mechanisms and complex kinematic chains that perform a specific task. Within the different types of electric motors, induction machines have been widely used in a wide variety of industrial processes. This is mainly due to its excellent features and benefits, such as robustness, easy control, simplicity, reliability, and easy maintenance. However, despite its high robustness, this type of machine is subject to mechanical, thermal, electrical, and environmental stress under various operating conditions during its useful life, which inevitably leads to failures. The main failures that usually occur in electric induction motors are those related to their internal components, such as broken rotor bars, wear on the outer and inner races of the bearings, wear on the bearing cage, stator failures ( for example, short circuit between adjacent turns), among others. When an electric motor is operating under some fault condition, its performance may be affected, which translates into higher energy consumption, causing extra costs at the time of billing. This research thesis presents the development of an innovative methodology capable of automatically generating an adequate diagnosis of the occurrence of the most common failures that can develop in electric induction motors under various operating conditions through the analysis of the stray magnetic flux (which can be captured at the periphery of the motor frame). This is performed by using time-frequency decomposition tools, data classification tools, and signal characterization parameters. Thus, the validity of techniques based on the analysis of the stray magnetic flux for the diagnosis of different electromechanical failures in induction motors is tested. The results demonstrate the excellent performance of the proposed automatic diagnosis methodology when evaluated in a wide variety of engines with different construction characteristics.Zamudio Ramírez, I. (2023). Diagnóstico de fallos electromecánicos en motores eléctricos mediante el análisis avanzado del flujo magnético y su implementación en hardware [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19798

Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis

This research was funded by ¿GENERALITAT VALENCIANA, Conselleria de Educación, Investigación, Cultura y Deporte, grant number AICO/2019/224¿Zamudio-Ramírez, I.; Osornio-Ríos, RA.; Antonino-Daviu, JA.; Quijano-Lopez, A. (2020). Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis. Sensors. 20(5):1-19. https://doi.org/10.3390/s2005147711920

DEVELOPMENT OF A WIRELESS SIGNAL ACQUISITION SYSTEM FROM SENSORS FOR COMFORT AND ENERGY QUALITY

AbstractThe acquisition of wireless signals from sensors represents a variety of advantages over cable communication systems. This work presents a ZigBee-based signal acquisition system that takes advantage of its features to make a flexible system that can be used in different fields without the necessary use of a PC since a touchscreen and a microcontroller is used. The system is implemented in a building to monitor all the physical variables that are referred for the comfort of people, such as luminosity, temperature, humidity, gas concentration, smoke, human presence, glass breakage among others. The measure of these variables also could contribute to define or activate some extra-functions of the system, for example, alarms in case of fire presence. The system stores information of all sensors of all the network created in a Micro SD and uses it to make plots, also it is possible to visualize real-time readings.Keywords: Touchscreen, wireless sensor network (WSN), ZigBee.DESARROLLO DE UN SISTEMA DE ADQUISICIÓN DE SEÑALES INALÁMBRICAS A PARTIR DE SENSORES PARA COMODIDAD Y CALIDAD ENERGÉTICAResumenLa adquisición de señales inalámbricas de sensores representa una variedad de ventajas sobre los sistemas de comunicación por cable. Este trabajo presenta un sistema de adquisición de señales basado en antenas ZigBee que aprovecha sus características para hacer un sistema flexible que puede ser utilizado en diferentes campos sin el uso necesario de una PC ya que se utiliza una pantalla táctil y un microcontrolador. El sistema es implementado en un edificio para monitorear todas las variables físicas que se refieren a la comodidad de las personas, tales como luminosidad, temperatura, humedad, concentración de gas, humo, presencia humana, rotura de vidrios, entre otros. La medición de estas variables también es utilizada para activar algunas funciones extras del sistema, por ejemplo, alarmas en caso de presencia de fuego. El sistema almacena información de todos los sensores de toda la red creada en una Micro SD y crea gráficos históricos de dichas variables, además, es posible visualizar lecturas en tiempo real.Palabras claves: Pantalla táctil, red de sensores inalámbrica, ZigBee

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

Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis

Induction motors are essential and widely used components in many industrial processes. Although these machines are very robust, they are prone to fail. Nowadays, it is a paramount task to obtain a reliable and accurate diagnosis of the electric motor health, so that a subsequent reduction of the required time and repairing costs can be achieved. The most common approaches to accomplish this task are based on the analysis of currents, which has some well-known drawbacks that may lead to false diagnosis. With the new developments in the technology of the sensors and signal processing field, the possibility of combining the information obtained from the analysis of different magnitudes should be explored, in order to achieve more reliable diagnostic conclusions, before the fault can develop into an irreversible damage. This paper proposes a smart-sensor that explores the weighted analysis of the axial, radial, and combination of both stray fluxes captured by a low-cost, easy setup, non-invasive, and compact triaxial stray flux sensor during the start-up transient through the short time Fourier transform (STFT) and characterizes specific patterns appearing on them using statistical parameters that feed a feature reduction linear discriminant analysis (LDA) and then a feed-forward neural network (FFNN) for classification purposes, opening the possibility of offering an on-site automatic fault diagnosis scheme. The obtained results show that the proposed smart-sensor is efficient for monitoring and diagnosing early induction motor electromechanical faults. This is validated with a laboratory induction motor test bench for individual and combined broken rotor bars and misalignment faults

Smart Sensor for Fault Detection in Induction Motors Based on the Combined Analysis of Stray-Flux and Current Signals: A Flexible, Robust Approach

[EN] The most recent trend in the electric motor condition monitoring area relies on combining the information obtained from different machine quantities in order to reach a more reliable conclusion about the motor¿s health. This knowledge is of critical importance nowadays, especially in industrial applications in which unexpected outages can lead to severe repercussions. This paper presents a new intelligent sensor that combines, in a single unit, the information obtained from the analysis of stray fluxes (both axial and radial) and currents by means of a feed-forward neural network (FFNN) for classification purposes. Unlike other solutions, the sensor is based on the application of advanced signal processing tools that are adapted to the online analysis of these quantities under transient from a single processing unit (smart sensor). The combination of these new tools with the classical steady-state analysis of such quantities enables to obtain a more reliable conclusion on the motor health. The experiments included in the paper demonstrate the reliability provided by the sensor, which is being prepared to incorporate a third input based on infrared data.This work was derived from a project supported by the Beca Leonardo a Investigadores y Creadores Culturales 2019 of Fundacion BBVA (ref: IN[19]_ ING_ING_0083). The BBVA Foundation is not responsible for the opinions, comments, or contents included in the project or the results derived from it. These are the exclusive responsibility of the authors. The authors would like to thank Consejo Nacional de Ciencia y Tecnologia (scholarship with key code 2019-000037-02NACF).Zamudio-Ramírez, I.; Osornio-Rios, RA.; Antonino-Daviu, JA. (2022). Smart Sensor for Fault Detection in Induction Motors Based on the Combined Analysis of Stray-Flux and Current Signals: A Flexible, Robust Approach. IEEE Industry Applications Magazine. 28(2):56-66. https://doi.org/10.1109/MIAS.2021.3114647566628

Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux

[EN] Induction motors (IMs) are essential components in industrial applications. These motors have to perform numerous tasks under a wide variety of conditions, which affects performance and reliability and gradually brings faults and efficiency losses over time. Nowadays, the industrial sector demands the necessary integration of smart-sensors to effectively diagnose faults in these kinds of motors before faults can occur. One of the most frequent causes of failure in IMs is the degradation of turn insulation in windings. If this anomaly is present, an electric motor can keep working with apparent normality, but factors such as the efficiency of energy consumption and mechanical reliability may be reduced considerably. Furthermore, if not detected at an early stage, this degradation could lead to the breakdown of the insulation system, which could in turn cause catastrophic and irreversible failure to the electrical machine. This paper proposes a novel methodology and its application in a smart-sensor to detect and estimate the healthiness of the winding insulation in IMs. This methodology relies on the analysis of the external magnetic field captured by a coil sensor by applying suitable time-frequency decomposition (TFD) tools. The discrete wavelet transform (DWT) is used to decompose the signal into different approximation and detail coefficients as a pre-processing stage to isolate the studied fault. Then, due to the importance of diagnosing stator winding insulation faults during motor operation at an early stage, this proposal introduces an indicator based on wavelet entropy (WE), a single parameter capable of performing an efficient diagnosis. A smart-sensor is able to estimate winding insulation degradation in IMs using two inexpensive, reliable, and noninvasive primary sensors: a coil sensor and an E-type thermocouple sensor. The utility of these sensors is demonstrated through the results obtained from analyzing six similar IMs with differently induced severity faults.We would like to thank Consejo Nacional de Ciencia y Tecnologia (CONACYT) for providing economic support in this work (scholarship). Finally, thanks to the next projects: SEP-CONACYT 222453-2013, and FOFIUAQ-FIN201812. This wok was also funded by Spanish 'Ministerio de Ciencia Innovacion y Universidades' and FEDER 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' (ref: PGC2018-095747-B-I00).Zamudio-Ramírez, I.; Osornio-Rios, RA.; Trejo-Hernandez, M.; Romero-Troncoso, RDJ.; Antonino-Daviu, J. (2019). Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux. Energies. 12(9). https://doi.org/10.3390/en1209165812

Methodology for Tool Wear Detection in CNC Machines Based on Fusion Flux Current of Motor and Image Workpieces

In the manufacturing industry, computer numerical control (CNC) machine tools are of great importance since the processes in which they are used allow the creation of elements used in multiple sectors. Likewise, the condition of the cutting tools used is paramount due to the effect they have on the process and the quality of the supplies produced. For decades, methodologies have been developed that employ various signals and sensors for wear detection, prediction and monitoring; however, this field is constantly evolving, with new technologies and methods that have allowed the development of non-invasive, efficient and robust systems. This paper proposes the use of magnetic stray flux and motor current signals from a CNC lathe and the analysis of images of machined parts for wear detection using online and offline information under the variation in cutting speed and tool feed rate. The information obtained is processed through statistical and non-statistical indicators and dimensionally reduced by linear discriminant analysis (LDA) and a feed-forward neural network (FFNN) for wear classification. The results obtained show a good performance in wear detection using the individual signals, achieving efficiencies of 77.5%, 73% and 89.78% for the analysis of images, current and stray flux signals, respectively, under the variation in cutting speed, and 76.34%, 73% and 63.12% for the analysis of images, current and stray flux signals, respectively, under the variation of feed rate. Significant improvements were observed when the signals are fused, increasing the efficiency up to 95% for the cutting speed variations and 82.84% for the feed rate variations, achieving a system that allows detecting the wear present in the tools according to the needs of the process (online/offline) under different machining parameters