Especialización Gerencia de Mantenimiento y Gestión de Activos

URI permanente para esta colecciónhttp://hdl.handle.net/11634/56080

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Aplicación De Inteligencia Artificial Y Analítica Predictiva Para Optimizar La Gestión De Mantenimiento De Maquinaria Amarilla Pesada En El Departamento Del Huila
(Universidad Santo Tomás, 2026-07-17) Cangrejo Manrique, Arles Miguel; Universidad Santo Tomás
In the Huila Department, there's a large fleet of heavy machinery that includes backhoes, motor graders, loaders, bulldozers, and dump trucks, which are essential for road infrastructure and public works. However, there has been a noticeable decline in the performance and availability of this machinery, which shows repeated breakdowns, long downtime, and rising repair costs, causing delays in projects and financial losses. This problem is worsened by a lack of technical and logistical management, as well as maintenance policies and a lack of a predictive maintenance culture. In response to this situation, the project 'Implement an Artificial Intelligence and Predictive Analytics Model to Optimize Maintenance Management of Heavy Machinery in Huila' was developed, using the analysis of historical data, oil analysis, vibrations, and maintenance records, with the aim of estimating equipment lifespan and improving operational decision-making. The proposed procedure is based on the ISO14224 technical standard, which implements monitoring of critical variables (vibration, temperature, lubrication) allowing the government entity to anticipate failures and extend the life of the equipment.
Análisis de modos de falla en barrajes de centros de control de motores de baja tensión desde la confiabilidad eléctrica
(Universidad Santo Tomás, 2026-04-06) Delgado Delgado, Juan Diego; Urrea Morales, Jair Leonardo; Universidad Santo Tomás; https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001982561
Nuevas tendencias de mantenimiento inteligente en flotas de vehículos eléctricos, híbridos y de combustión interna
(Universidad Santo Tomás, 2026-03-28) Gutiérrez Cardozo, Juan José; Vergara Lozano, Giovanny Andrés; Universidad Santo Tomás; https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001706227; https://scholar.google.com/citations?hl=es&authuser=1&user=Zq5r6LYAAAAJ; https://orcid.org/0000-0002-7455-882X
Fleet maintenance management has undergone a significant transformation in recent years due to the integration of advanced technologies associated with electrification, digitalization, and asset automation. The coexistence of internal combustion, hybrid, and electric vehicles within the same fleet has increased technical, operational, and economic complexity, making traditional corrective and time-based preventive maintenance models insufficient to ensure high levels of availability, reliability, and sustainability. In this context, this monograph analyzes emerging trends in intelligent maintenance applied to mixed vehicle fleets, with particular emphasis on Reliability-Centered Maintenance (RCM), artificial intelligence and machine learning-based predictive maintenance, as well as solutions based on the Internet of Things, digital twins, and augmented reality. The study is developed through a systematic literature review of scientific papers, international conferences, and academic theses published between 2017 and 2025, aiming to identify the most relevant methodologies, their technical outcomes, and implementation feasibility in real operational environments. The comparative analysis reveals that RCM enables effective prioritization of critical components, optimization of maintenance resources, and reduction of recurrent failures across different powertrain technologies, standing out as a cost-effective and adaptable strategy. Additionally, artificial intelligence-based maintenance models, particularly deep neural networks, demonstrate diagnostic accuracy levels above 97%, allowing early failure detection, reduction of unplanned downtime, and optimization of asset life cycles. Furthermore, the integration of IoT technologies, digital twins, and augmented reality strengthens the Maintenance 4.0 paradigm by enabling real-time monitoring, failure scenario simulation, and interactive technical assistance for operators and drivers. The findings indicate that adopting these intelligent maintenance strategies significantly improves operational and economic performance while supporting environmental regulations and sustainable mobility objectives, especially in regions with demanding operational conditions such as the Eastern Plains of Colombia.
Estrategias de mejora en mantenimiento a equipos críticos en las plantas extractoras de Colombia
(Universidad Santo Tomás, 2026-04-25) Cisneros Londoño, Sergio; Bueno Zarate, Yeison; Universidad Santo Tomás
Estrategia de Monitoreo Predictivo para Gestión de Activos - Horno Túnel Industrial de 30m
(Universidad Santo Tomás, 2026-07-10) Rodríguez Morales, Jonnathan Gustavo; Roa Montoya, Cristian Camilo; Leal Téllez, Gustavo Andrés; Universidad Santo Tomás; https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001687224
The present document develops a Condition-Based Maintenance (CBM) strategy for a 30-meter industrial tunnel oven, within the framework of the Academic Mission 2026 — Graduation Option. The focus of the work is the methodological contribution: the integration of reliability tools (Weibull, MTBF/MTTR, FMEA, RCA, RCM) into a condition monitoring system aligned with ISO 55001:2014 and ISO 17359:2018. The sensor technology employed is a means of implementation, not the central objective.
Propuesta de implementación de la filosofía kaisen para la mejora continua en el mantenimiento de acabados en el sector hotelero
(Universidad Santo Tomás, 2026-07-10) Barrios Redondo, Diego Armando; Universidad Santo Tomás
In hotel maintenance management, the recurrent deterioration of structural finishes is often attributed to technical deficiencies in maintenance execution, overlooking the impact of daily human behavior on space preservation. In continuously operating urban hotels, this situation leads to frequent rework, affecting maintenance sustainability and the perception of order and quality. This article aims to analyze the implementation of the Kaizen philosophy as a continuous improvement strategy applied to structural maintenance, with a focus on awareness and shared responsibility in the use of maintained spaces. The study follows an applied academic approach through a case study conducted at the Best Western Plus Santa Marta Hotel, based on direct observation and the operational experience of the maintenance department. As a result, a conceptual proposal is presented that integrates Kaizen principles and 5S-related practices to strengthen post-maintenance care culture, reduce rework, and extend the service life of architectural finishes. It is concluded that continuous improvement focused on human behavior, rather than solely on technical interventions, represents a feasible approach to enhancing structural maintenance management in the hospitality sector.
Análisis Vibracional Como Técnica de Monitoreo de Condición para Apoyar la Gestión de Activos Rotativos Críticos en Colombia
(Universidad Santo Tomás, 2026-07-10) Franco Carreño, Juan Felipe; Pineda Blanco, Maria Magdalena; Universidad Santo Tomás
The International Academic Mission conducted in Mexico provided first-hand exposure to different experiences related to maintenance, reliability, and asset management. During the visits, vibration analysis stood out due to its application in condition monitoring and fault diagnosis of critical rotating machinery. These assets may experience degradation mechanisms such as imbalance, misalignment, and bearing defects, which can be detected through vibration analysis, supporting condition-based maintenance decision-making (Mobley, 2002; Randall, 2011; ISO 17359, 2018). This experience motivated an analysis of the opportunities and potential of vibration analysis within the Colombian industrial context. The study was developed based on the knowledge and observations acquired during the academic mission and was complemented by a literature review, including a comparative analysis between the applications observed in Mexico and the implementation opportunities identified in Colombia. The results are intended to establish application criteria for asset selection, the use of vibration analysis, and condition-based maintenance decision-making, in accordance with the asset management principles defined by ISO 55000 (ISO, 2014). Keywords: vibration analysis, condition monitoring, asset management, critical rotating assets, reliability
Marco de Referencia de un Sistema de Gestión de Activos para el Sector Cafetero en la Finca la Armonía, en Supatá, Cundinamarca, Aplicación de Principios Conceptuales ISO 55000
(Universidad Santo Tomás, 2026-06-06) Gómez Cortes, Hancel Raul; Muñoz Barajas, Helver Mauricio; Universidad Santo Tomás; https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001446983
The purpose of this paper is to develop a framework for an asset management system based on the guidelines of ISO 55000 and ISO 55001, and to use these standards to conduct a simulation of a case study applied to the La Armonía farm, all of this presented in a flowchart and following a structure divided into several phases, where diagrams, tables, and images of the assets are shown, along with the relationship between KPIs and the agricultural sector, specifically applied to the case study. Consequently, the advantages and disadvantages of this applied framework are demonstrated, as it highlights the farm’s shortcomings but also demonstrates the feasibility of maximizing those assets by generating greater control over their maintenance and enabling decisions based on previously obtained information. Although the system requires more time and greater data collection for more accurate results, its functionality is evident.
Manual de aplicación integrada de las metodologías Balanced Scorecard (BSC) y AMORMS para la identificación de KPI en la gestión de activos físicos en MiPymes avícolas del Meta.
(Universidad Santo Tomás, 2026-06-05) Parra Zamudio, Onman Andrés; Córdoba Malaver, Ana Rocío; Universidad Santo Tomás; https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000081827; https://orcid.org/0000-0002-5687-4378
The development of the present monograph proposes a diagnostic and management model for the formulation of the strategic map for agribusiness micro and small enterprises (MiPymes), integrating the Balanced Scorecard (BSC) and AMORMS methodologies, aligned with the ISO 55000 series. The study was applied to a poultry farm in the department of Meta, allowing the identification of administrative management indicators and Key Performance Indicators (KPI) associated with the management of physical assets, thereby enabling the evaluation of the administrative and technical maturity of the microenterprise. Based on the correlation of the BSC and AMORMS methodologies, the SWOT matrix is employed to analyze the financial, internal process, customer, human talent, and environmental-social perspectives; through the study of these methodologies, it becomes possible to demonstrate the transition from an empirical business model to a management approach with professional foundations, based on data and measurable results, optimizing decision-making, operational efficiency, and business sustainability. The present contribution to the body of knowledge in maintenance asset management, demonstrates that the correspondence between the BSC–AMORMS methodologies is an effective strategy to strengthen the management of the company and its assets; enhancing the main asset, which is human talent, as a strategic intangible resource capable of consolidating an efficient, organized, and effective corporate culture; using continuous improvement tools such as Kaizen to strengthen competitiveness and align the company’s objectives, with international market standards. Keywords: Balanced Scorecard (BSC), AMORMS Methodology, Asset Management, Administrative Management Indicators, Key Performance Indicators (KPI).
Análisis y Transferencia de Prácticas Internacionales en la Integración de Ensayos No Destructivos, Inteligencia Artificial Y Gemelos Digitales para el Mantenimiento Predictivo de Activos Industriales en Colombia
(Universidad Santo Tomás, 2026-04-01) Mendoza Sanabria, Laura Daniela; Cordoba Malaver, Ana Roció; Universidad Santo Tomás; https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000081827; https://orcid.org/0000-0002-5687-4378
Industrial asset management is currently grappling with the dual pressure of aging infrastructure and stringent regulatory compliance within a framework of cost optimization. While Non-Destructive Testing (NDT) remains a cornerstone for identifying structural integrity issues such as corrosion and fatigue, its traditional application—characterized by periodic inspections—fails to provide the real-time insights necessary for proactive intervention. In Colombia's mining, energy, and oil & gas sectors, unscheduled downtime accounts for productivity losses of up to 25% and drives operational budget overruns beyond 30%. This study emphasizes the strategic necessity of evolving toward predictive maintenance by integrating NDT data with digital twin technology and advanced modeling. By transforming static inspection results into dynamic inputs for virtual replicas, organizations can simulate asset behavior in real-time. This integration bridges the gap between technical diagnosis and high-level managerial decision-making, ensuring operational continuity and alignment with ISO 55000 sustainability standards.
Análisis del Impacto de los Macroambientes en la Transformación Digital del Mantenimiento en la Industria de Logística y Transporte
(Universidad Santo Tomás, 2026-03-19) Jimenez Pinzón, María Luisa; Muñoz Barajas, Helver Mauricio; Universidad Santo Tomás; https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001446983
The logistics and transportation industry faces increasing demands for operational efficiency, reliability, and sustainability, which is driving the adoption of technologies for the digital transformation of maintenance. In Colombia, this sector represents a strategic component for the country's economic competitiveness and the efficiency of its logistics operations. However, the adoption of these technologies is influenced by political, economic, social, technological, environmental, and legal (PESTEL) factors, whose specific impact on the sector still requires further analysis.
Mantenimiento Predictivo por Medio de IoT a Motor MTU
(Universidad Santo Tomás, 2026-02-05) Vargas Sandoval, Juan Sebastián; Fernández, Grenllery; Universidad Santo Tomás; https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001446660
In high-criticality industrial systems, internal combustion engines used in power generation play a fundamental role as strategic assets, since their level of reliability and availability directly affects the operational and financial results of organizations. In this sense, MTU engines constitute capital-intensive investments that require the adoption of management strategies aimed at risk mitigation and value maximization. According to the International Organization for Standardization, through the ISO 55000 standard, asset management goes beyond the physical maintenance of equipment and is conceived as a coordinated activity intended to balance costs, risks, and performance throughout the asset's life cycle (ISO, 2014). However, traditional preventive maintenance approaches show significant limitations when applied to scenarios characterized by load variability and changing operating conditions. In this context, Zambrano-Castro and Pérez-Guerrero (2021) point out that, in industrial diesel engines, exclusive reliance on maintenance scheduled by hours of operation can lead both to unnecessary interventions and to unforeseen failures. Consequently, the current trend is moving toward diagnostic schemes based on the actual condition of the equipment. This position is consistent with what Amendola (2020) states, who affirms that modern availability management requires surpassing traditional cyclical models in order to optimize the asset's life cycle. From this perspective, the Internet of Things (IoT) is consolidated as the main technological enabler of maintenance transformation (Red Hat, n.d.). In particular, Porter and Heppelmann (2015) highlight that the evolution towards smart and connected products allows the data generated by assets to cease being simple historical records and become strategic inputs with competitive advantage potential. Complementarily, the integration of sensors and advanced analytics tools promotes proactive maintenance management, in accordance with the ECLAC (2021) vision of digitalization as a mechanism to reduce operational uncertainty in the region's industry. From a financial perspective, it is essential that the implementation of IoT-based solutions is supported by criteria of profitability and value generation. In this regard, García Palencia (2012) emphasizes that any investment in monitoring systems must be evaluated based on financial indicators such as present value and reduction of economic risk, providing objective information for decision-making related to the continuity, modernization, or replacement of the asset. Despite the technological advances described, a significant gap persists between real-time condition monitoring and its effective integration into financial decision-making processes. Therefore, the aim of the present work is to develop and evaluate an IoT-based maintenance strategy applied to an MTU engine, oriented towards fault anticipation and cost optimization, under the guidelines established by the ISO 55000 standard.
Propuesta de Modelo de Digitalización de Datos Maestros, Históricos y de Condición en CMMS/EAM para la Toma de Decisiones en Gestión de Activos
(Universidad Santo Tomás, 2026-02-06) Suarez Rivera, Juan Sebastian; Universidad Santo Tomás
This poster presents a proposed model for the digitization of master data, historical work order data, and condition data in a CMMS/EAM and using them in asset management decisions based on risk, criticality, and TOTEX costs
Formulación de un plan de mantenimiento predictivo para un equipo de Soldadura por Fricción Rotacional
(Universidad Santo Tomás, 2025-05-02) Faustino Molina, Katherin Johanna; Martinez Sarache, Handel Andres; Universidad Santo Tomás; https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001467576; https://scholar.google.com/citations?user=cYDq-4YAAAAJ&hl=es; https://orcid.org/0000-0002-2879-4899
The rotary friction welding equipment, located in the metallurgical engineering laboratories of the Universidad Pedagógica y Tecnológica de Colombia, is a key tool in academic training and research on solid-state welding. To ensure the reliability of experimental results and extend the equipment's service life, it is essential to implement appropriate maintenance strategies. In this context, a predictive maintenance plan was designed, which, through visual inspections and on-site visits, allowed for the assessment of its operational status and working environment. The applied methodology follows the (ISO 14224, 2016) standard, including a taxonomic analysis, the definition of operational limits, and the coding of systems and components to facilitate failure analysis. Using a root cause diagram, failure mechanisms related to vibrations during welding, electrical and instrumentation failures, as well as issues caused by unauthorized handling, were identified. These findings were compared with Tables B.2 and B.3 of the (ISO 14224, 2016) standard. Based on the obtained results, specific predictive actions were proposed, such as thermography, magnetic particle testing, and hydraulic oil analysis, complemented by a standardized inspection format. The documentation of the operational history will allow for a more accurate analysis to efficiently schedule predictive maintenance while aligning with the available budget. Furthermore, this methodology not only optimizes the performance of the studied equipment but can also be applied to other machinery requiring evaluation and optimization.
Aplicación del Mantenimiento Predictivo en la Industria Petrolera: Una Revisión Exhaustiva
(Universidad Santo Tomás, 2025-05-28) Gutiérrez Jiménez, Yekini Mateo; Maldonado Moreno, Jerson Fabian; Universidad Santo Tomás; https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001712701; https://scholar.google.es/citations?hl=es&user=uRbUPY0AAAAJ&scilu=&scisig=AMD79ooAAAAAY4TC_QQaJ-vuzqcbyHlq4PrrSfuKuSSn&gmla=AJsN-F6MrzbVxRU5sjJmSAp44MFRC7hNNIktxgWtD8MYd8q6XsLGqNWOr61BiEPBA-MBsvBWsX01xFdJ6EWHM5l1YL4v8V7itEr0-6wHkrcfWRqXpEt3ZrW1tBWKORvpJcXsazcgI-27&sciund=7173997066276809121; https://orcid.org/0000-0002-4919-6150
Predictive maintenance has revolutionized the oil industry by optimizing asset management and reducing operational costs through the anticipation of failures in critical equipment. This article provides a comprehensive review of the technologies applied in the sector, highlighting the use of machine learning, neural networks, support vector machines, and Weibull analysis. Recent studies that have implemented these techniques to enhance the reliability of pumping systems, turbo compressors, and other key equipment are analyzed. The results demonstrate that the use of advanced algorithms enables highly accurate failure prediction, reducing downtime and optimizing maintenance decision-making. Finally, the main challenges in implementing these technologies are identified, and future research directions are proposed to improve their adoption in the oil industry.
Propuesta plan de negocios para la creación de una Empresa prestadora de servicios de consultoría, mantenimiento eléctrico y mecánico
(Universidad Santo Tomás, 2025-05-25) Cárdenas Nonsoque, Freyner Camilo; Moreno Castiblanco, Mayra Lorena; Carmona Rivera, Jairo Andres; Cetina Torres, Leonel; Universidad Santo Tomás; https://scholar.google.com/citations?user=131nUS4AAAAJ&hl=es
This work presents a business plan for the creation of a consulting and maintenance company specializing in electrical and mechanical systems, based on the growing need for specialized services that ensure the efficient and safe operation of organizations. The study begins by recognizing that many companies underestimate the importance of proper maintenance, which can lead to unforeseen costs and long-term risks. In response, a strategic solution is proposed that combines precise technical diagnostics, preventive maintenance, and customized programs tailored to each client's specific needs. The document is structured into several chapters that address the project's justification, the methodology used for market analysis, and the development of implementation strategies. It also includes a detailed study of the competition, the definition of the organizational structure, the necessary human resources, and a financial analysis that evaluates the economic feasibility of the project. Overall, this work provides a solid framework for the launch of a sustainable, efficient company that complies with the regulations of the electrical and mechanical maintenance sector.
Influencia del mantenimiento basado en condición de los lubricantes: una revisión sistemática en los motores de encendido por compresión
(Universidad Santo Tomás, 2025-05-14) Garrido Pérez, Wilson Enrique; Vera Rozo, James Donald; Universidad Santo Tomás; https://scholar.google.es/citations?user=OY25FjcAAAAJ&hl=es; https://orcid.org/0000-0003-0516-3936
Condition-Based Maintenance (CBM) has become a key strategy for optimizing the maintenance of compression ignition engines, allowing for the real-time assessment of lubricant condition and the adjustment of oil change intervals based on its degradation. This study presents a systematic review of the literature on CBM applied to lubricants, analyzing its impact on operational efficiency, cost reduction, and industrial sustainability. For this purpose, scientific databases were utilized to identify trends and technological advancements in lubricant tribology, considering methods such as spectroscopic, ferrographic, and viscosity analysis. The results show that CBM implementation reduces lubricant waste, extends engine lifespan, and lowers operational costs by minimizing unexpected failures. Additionally, the increasing adoption of IoT sensors and predictive algorithms for real-time oil monitoring is highlighted, improving maintenance management. Despite its advantages, challenges remain in standardizing degradation parameters and integrating advanced technologies for real-time monitoring across various industrial environments. Future research directions include the development of artificial intelligence models to predict lubricant degradation and the expansion of CBM into sectors such as Oil & Gas, aviation, and mining. This study provides a foundation for optimizing maintenance management and promoting more efficient and sustainable industry practices. Keywords: Tribology, condition-based maintenance, oil analysis, prediction, lubricant monitoring.
Revisión del Mantenimiento Predictivo Potenciado por Machine Learning en el Sector Industrial de América Latina: Situación Actual, Desafíos y Tendencia
(Universidad Santo Tomás, 2025-05-05) Romero Ramírez, Henry Esteban; Poveda Pachón, Marlon Yesid; Universidad Santo Tomás; https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0002132921; https://scholar.google.es/citations?user=5kKmXFkAAAAJ&hl=es&oi=ao; https://orcid.org/0009-0001-3180-7099
This study examines the current situation, challenges, and trends of predictive maintenance powered by Machine Learning in the industrial sector of Latin America through a systematic review that included 60 implementation cases identified in Scopus, ScienceDirect, and Google Scholar. Only documents published from 2014 onward in Spanish, English, or Portuguese were selected, all applying Machine Learning techniques to predict failures in real industrial equipment or systems within Latin American companies. The results show that Brazil accounts for the largest number of publications (48%), followed by Ecuador (18%) and Colombia (15%), while Mexico, Argentina, Chile, and Peru represent 19% of the cases. The adoption of these technologies mainly focuses on the manufacturing, energy, and automotive industries, where critical equipment such as turbines, wind turbines, and heavy machinery receive the most attention. Among the most widely used Machine Learning techniques are Random Forests (RF), Support Vector Machines (SVM), and Decision Trees (DT), with Python as the predominant programming language due to its accessibility and versatility. The main barriers identified include the lack of technological infrastructure, managerial unawareness, resistance to change, and data confidentiality. Nevertheless, future initiatives are expected to prioritize solutions related to waste management, resource optimization, and enhanced energy sustainability, offering opportunities to transform industrial processes and improve regional competitiveness.

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