Sistema de Diagnóstico Distribuido de Motores de Inducción basado en Redes Inalámbricas de Sensores y Protocolo ZigBee
| dc.contributor.advisor | Rosero García, Javier Alveiro | spa |
| dc.contributor.author | Caballero Peña, Jairo Andrés | spa |
| dc.date.accessioned | 2020-02-20T15:58:17Z | spa |
| dc.date.available | 2020-02-20T15:58:17Z | spa |
| dc.date.issued | 2020-01-10 | spa |
| dc.description.abstract | La inclusión de las redes inalámbricas de sensores y tecnologías IoT (Internet of Things) en ambientes industriales busca el monitoreo y registro autónomo de una mayor cantidad de variables del proceso industrial con una alta confiabilidad y resiliencia, además, procuran realizar un análisis previó para obtener parámetros de las señales que puedan dar a conocer una mejor descripción del estado del sistema y su condición de operación. Esto permite reducir el consumo de energía al disminuir la transmisión de datos netos medidos con paquetes hasta mil veces más largas que un parámetro calculado desde el sensor hacia los centros de control. La finalidad del monitoreo propuesto es el análisis para la identificación de anomalías que puedan afectar la disponibilidad de la planta o incrementar los costos de producción, y mejorar los procesos de mantenimiento. En este proyecto se desarrolló un sistema de monitoreo y diagnóstico remoto basado en una red de sensores cuyos nodos remotos se encarguen de la recolección de datos y su posterior análisis para la identificación de anomalías que representen fallas críticas para el proceso o sistema industrial. El sistema propuesto se enfocó en el diagnóstico de falla de motores de inducción debido a que representan el mayor porcentaje de equipos en aplicaciones industriales. El proyecto se limitó a la identificación de falla entre espiras (2, 4 y 6 espiras), como un antecedente de fallas críticas, corto circuito fase-fase y corto circuito fase-tierra al presentarse como un deterioro del aislamiento. Se empleo el método de análisis de corriente de estator (MCSA). El nodo remoto inteligente se implementó con MCU LPCXpresso54114 con conexión a una red inalámbrica de sensores basada en protocolo ZigBee mediante tarjetas de comunicación XBee. El nodo concentrador (gateway) está compuesto de una tarjeta Raspberrry PI con comunicación mediante protocolo HTTP y formato JSON (PI Web API) a la base de datos del sistema de monitoreo industrial PI System El diagnóstico se ejecuta de manera remota por medio de un análisis preliminar para el cálculo de indicadores de falla y luego mediante SVM (Support Vector Machine) se clasifican los datos en comportamientos conocidos de condiciones de falla. Se plantearon indicadores basados en la medición neta de las corrientes, FFT (Fast Fourier Transform) y DWT (Discrete Wavelet Transform). Se realizó validación en laboratorio de la clasificación en tiempo real de fallas entre espiras aplicadas a un motor de inducción tipo jaula de ardilla, comparando diferentes configuraciones del diagnóstico, del análisis para la extracción de indicadores y de los indicadores de falla empleados; permitiendo plantear mejoras para la reducción de los porcentajes de error por falsa detección de falla, o no detección de falla. Estos avances finalmente se traducen a incrementar la confiabilidad del diagnóstico, la observabilidad de la falla, la diferenciación entre condiciones de falla, la precisión de la clasificación, la tolerancia a transitorios, sensibilidad, entre otros. | spa |
| dc.description.abstract | The inclusion of sensors wireless networks and Internet of Things (IoT) technologies in industrial environments seeks an autonomous monitoring and storage with high reliability and resilience of a greater number of industrial process variables, in addition, they attempt to perform a preliminary analysis to obtain parameters of the signals that can give a better description of system state and its operation condition. This allows reducing energy consumption by decreasing the transmission of raw data, a parameter calculated from the sensor to the control centers in change of a thousand times longer package. The purpose of the proposed monitoring is the analysis for the identification of anomalies that may affect the availability of the plant or increase production costs and improve maintenance processes. In this project, a remote fault diagnosis and monitoring system based on wireless sensor networks was developed whose remote nodes are responsible for data collection and analysis for the identification of anomalies over industrial process or system, previously to critical faults. The proposed system was focused on the induction motor fault diagnosis because these represent the highest percentage of equipment in industrial applications. This project was based on identify interturn faults (2, 4 and 6 turns) using Motor Current Signature Analysis (MCSA), because of the Interturn faults are produced by insulation deterioration and evolve in critical faults, phase to phase short-circuit and ground fault. The developed intelligent remote node was implemented with MCU LPCXpresso54114 with connection to a ZigBee protocol wireless sensor network through XBee communication module. The gateway node is a Raspberrry PI with communication through HTTP protocol and JSON format (PI Web API) to the PI System database (industrial monitoring system). The diagnosis is remotely executed, where a preliminary analysis is applied to calculate fault indicators. Then, with a SVM (Support Vector Machine), the data are classified in known behavior of fault conditions. Different fault indicators were proposed based on current measurement’s raw data, FFT (Fast Fourier Transform) and DWT (Discrete Wavelet Transform). Real time interturn fault classification was validated in laboratory with a squirrel cage induction motor comparing different settings and configuration of diagnosis, analysis for indicators extraction and testing diversified fault indicators. This allowed proposing improvements to reduce of error percentage by false detection or missing detection. The progress finally are reflected in increase the diagnosis reliability, the observability of the failure, the differentiation between fault conditions, classification accuracy, tolerance to transients, sensitivity, etc. | spa |
| dc.description.additional | Magíster en Ingeniería Eléctrica. | spa |
| dc.description.degreelevel | Maestría | spa |
| dc.format.extent | 168 | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/75656 | |
| dc.language.iso | spa | spa |
| dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá | spa |
| dc.publisher.department | Departamento de Ingeniería Eléctrica y Electrónica | spa |
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| dc.rights | Derechos reservados - Universidad Nacional de Colombia | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.license | Atribución-NoComercial-SinDerivadas 4.0 Internacional | spa |
| dc.rights.spa | Acceso abierto | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | spa |
| dc.subject.ddc | Ingeniería y operaciones afines::Otras ramas de la ingeniería | spa |
| dc.subject.proposal | Análisis distribuido | spa |
| dc.subject.proposal | Corriente de estator | spa |
| dc.subject.proposal | Induction Motor | eng |
| dc.subject.proposal | Motor-Current Signature Analysis | spa |
| dc.subject.proposal | Motor de Inducción | spa |
| dc.subject.proposal | Wireless Sensor Networks | eng |
| dc.subject.proposal | ZigBee | spa |
| dc.title | Sistema de Diagnóstico Distribuido de Motores de Inducción basado en Redes Inalámbricas de Sensores y Protocolo ZigBee | spa |
| dc.type | Documento de trabajo | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_8042 | spa |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
| dc.type.content | Text | spa |
| dc.type.driver | info:eu-repo/semantics/workingPaper | spa |
| dc.type.redcol | http://purl.org/redcol/resource_type/WP | spa |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
| oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |