Publication: Digital Twin de brazos robóticos: aplicación al mantenimiento predictivo digital
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2020
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Abstract
La evolución de la industria y la integración de robots a los procesos industriales ha marcado la cuarta revolución industrial denominada Industry 4.0. La integración de nuevas tecnologías a los procesos industriales como el Internet of Things (IoT) permite la interconexión de dispositivos, mejor monitoreo y análisis del estado actual de cada una de las partes del proceso, lo que da paso a la implementación de Digital Twins (DT). Un DT es una representación del estado actual y/o comportamiento de un dispositivo o sistema.
En los procesos industriales el mantenimiento de los equipos es una de las partes fundamentales, ya que una parada de producción no deseada representa grandes pérdidas de dinero, es por esto por lo que es de suma importancia saber cuándo un equipo está funcionando mal para realizar un mantenimiento. Esto se consigue con el mantenimiento predictivo, que permite tomar decisiones del proceso gracias a los datos generados del DT y aplicando técnicas de Inteligencia Artificial.
En el presente proyecto se realiza un DT con el fin de realizar mantenimiento predictivo para evitar paradas de planta no deseadas y pérdidas de equipos. La adquisición de datos se lo realiza por BLE desde un nodo sensor genérico y se envía mediante MQTT a un servidor, donde se almacena todos los datos en MongoDB. Para la visualización de la información y del estado actual del sistema se desarrolló un dashboard en Ignition, un software SCADA, que se ocupa a nivel industrial.
The evolution of the industry and the integration of robots into industrial processes has marked the fourth industrial revolution called Industry 4.0. The integration of new technologies to industrial processes, such as the Internet of Things (IoT), allows the interconnection of devices, better monitoring, and analysis of the current status of each of the parts of the process. This leads to the implementation of Digital Twins (DT), which is a representation of the current state and/or behavior of a device or system. In industrial processes, equipment maintenance is one of the fundamental parts because an unwanted stop production represents large losses of money. This is the reason which why it is very important to know when equipment is malfunctioning to carry out a maintenance. This is achieved with predictive maintenance, which allows to make decisions made in the process thanks to the data generated from the DT and the application of Artificial Intelligence techniques. In this project, a DT is carried out in order to perform predictive maintenance for avoiding unwanted plant shutdowns and equipment losses. Data acquisition is done by BLE from a generic sensor node and sent via MQTT to a server, where all the data is stored in MongoDB. To display the information and the current state of the system, a dashboard was developed in Ignition, a SCADA software, which is used at the industrial level.
The evolution of the industry and the integration of robots into industrial processes has marked the fourth industrial revolution called Industry 4.0. The integration of new technologies to industrial processes, such as the Internet of Things (IoT), allows the interconnection of devices, better monitoring, and analysis of the current status of each of the parts of the process. This leads to the implementation of Digital Twins (DT), which is a representation of the current state and/or behavior of a device or system. In industrial processes, equipment maintenance is one of the fundamental parts because an unwanted stop production represents large losses of money. This is the reason which why it is very important to know when equipment is malfunctioning to carry out a maintenance. This is achieved with predictive maintenance, which allows to make decisions made in the process thanks to the data generated from the DT and the application of Artificial Intelligence techniques. In this project, a DT is carried out in order to perform predictive maintenance for avoiding unwanted plant shutdowns and equipment losses. Data acquisition is done by BLE from a generic sensor node and sent via MQTT to a server, where all the data is stored in MongoDB. To display the information and the current state of the system, a dashboard was developed in Ignition, a SCADA software, which is used at the industrial level.
Description
Máster en Internet de las Cosas, Facultad de Informática UCM, Departamento de Arquitectura de Computadores y Automática, Curso 2019/2020.