Thesis Identificar y estudiar herramientas de inteligencia artificial para la aplicación en un modelo de gestión de activos y mantenimiento (MGAM) de ocho etapas
Loading...
Date
2025-05
Journal Title
Journal ISSN
Volume Title
Program
Ingeniería Civil Mecánica
Departament
Campus
Campus Casa Central Valparaíso
Abstract
Actualmente la tecnología de Inteligencia Artificial (IA) se esta masificando en su uso en diversos ámbitos desde personales hasta industriales. En esta memoria de titulación, se identificó que herramientas de IA a la fecha desarrolladas, pueden ser aplicadas a un MODELO DE GESTIÓN DE ACTIVOS Y MANTENIMIENTO (MGAM). Esto fue para conocer los alcances con los cuales prontamente se van a aplicar las tecnologías de IA a la industria en el área de mantenimiento. Además, adicionalmente se cuantificó si hay mejoras en: la toma de decisiones; reducción de tiempos de inactividad; la confiabilidad operacional y la eficiencia operativa. Para lograr esto, primero se estudio y analizó: el estado del arte de las IA en el ámbito de la Ingeniería para luego facilitar la búsqueda de herramientas de IA que se adecúen a los propósitos de un MGAM. Continuamente, se analizo funcionalmente y procesalmente el MGAM de ocho (8) etapas; con esto se logró identificar qué funciones de las IA se deben cumplir y cómo se deben integrar en los procesos específicos del MGAM. Posteriormente, se investigo la información disponible a nivel mundial para identificar las herramientas IA que se adecúan al MGAM. Seguidamente, se evaluó la factibilidad técnica de cada IA encontrada para determinar si la herramienta de verdad puede ser un aporte al proceso del MGAM. Luego, se evaluó y desarrolló un modelo de integración de las IA factibles técnicamente al proceso del MGAM. A continuación, se analizaron los beneficios y desafíos de las IA en el proceso del MGAM. Posteriormente, se propuso un plan de implementación y evaluación para el modelo de IA. Finalmente, se estudiaron los beneficios productivos, económicos y técnicos de aplicar las herramientas IA al MGAM. De acuerdo a este estudio, se concluye que las herramientas IA enumeradas se podrían integrar de manera beneficiosa a la implementación del MGAM de ocho etapas: ChatGPT, análisis de componentes principales aumentado con IA, aprendizaje de transferencia, Blockchain y modelado de información de construcción aumentado con IA.
Currently Artificial Intelligence (AI) technology is becoming widespread in its use in various areas from personal to industrial, in this degree report it was identified which AI tools developed to date, can be applied to an asset and maintenance management model (AMMM), this was to know the scope with which AI technologies will soon be applied to the industry in the maintenance area. In addition, it was additionally quantified if there are improvements in: decision-making; reduced downtime; operational reliability and operational efficiency. To achieve this, the state of the art of AI in the field of Engineering was first studied and analyzed, and then facilitated the search for AI tools that fit the purposes of an AMMM. Continuously, the eight (8) stage AMMM was analyzed functionally and procedurally; with this, it was possible to identify which functions of the AI must be fulfilled and how they must be integrated into the specific processes of the AMMM. Subsequently, the information was investigated globally to identify AI tools that are suitable for MGAM. Next, the technical feasibility of each AI found was evaluated to determine if the tool can really be a contribution to the AMMM process. Then, a model for the integration of technically feasible AIs into the AMMM process was evaluated and developed. Next, the benefits and challenges of AI in the AMMM process were analyzed. Subsequently, an implementation and evaluation plan for the AI model was proposed. Finally, the productive, economic and technical benefits of applying AI tools to AMMM were studied. According to this study, it is concluded that the AI tools listed could be beneficially integrated into the implementation of the eight-stage AMMM: ChatGPT, principal component analysis enhanced with AI, transfer learning, Blockchain and building information modeling enhanced with AI.
Currently Artificial Intelligence (AI) technology is becoming widespread in its use in various areas from personal to industrial, in this degree report it was identified which AI tools developed to date, can be applied to an asset and maintenance management model (AMMM), this was to know the scope with which AI technologies will soon be applied to the industry in the maintenance area. In addition, it was additionally quantified if there are improvements in: decision-making; reduced downtime; operational reliability and operational efficiency. To achieve this, the state of the art of AI in the field of Engineering was first studied and analyzed, and then facilitated the search for AI tools that fit the purposes of an AMMM. Continuously, the eight (8) stage AMMM was analyzed functionally and procedurally; with this, it was possible to identify which functions of the AI must be fulfilled and how they must be integrated into the specific processes of the AMMM. Subsequently, the information was investigated globally to identify AI tools that are suitable for MGAM. Next, the technical feasibility of each AI found was evaluated to determine if the tool can really be a contribution to the AMMM process. Then, a model for the integration of technically feasible AIs into the AMMM process was evaluated and developed. Next, the benefits and challenges of AI in the AMMM process were analyzed. Subsequently, an implementation and evaluation plan for the AI model was proposed. Finally, the productive, economic and technical benefits of applying AI tools to AMMM were studied. According to this study, it is concluded that the AI tools listed could be beneficially integrated into the implementation of the eight-stage AMMM: ChatGPT, principal component analysis enhanced with AI, transfer learning, Blockchain and building information modeling enhanced with AI.
Description
Keywords
Inteligencia artificial, Gestión de activos, Mantenimiento (MGAM), Reducción de tiempos de inactividad, Eficiencia operativa