Mantenimiento Predictivo por Medio de IoT a Motor MTU

Abstract

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.