Tecnologías de la industria inteligente aplicadas a las técnicas de monitoreo de condición en equipos mineros: Una revisión sistemática

Abstract

The mining industry has historically been one of the pillars of the global economy; however, its nature presents unique challenges regarding Asset Management. With the consolidation of the Fourth Industrial Revolution, new opportunities have arisen to enhance the performance of operations and maintenance activities, such as equipment condition monitoring. Therefore, this article aims to characterize the smart industry technologies used in condition monitoring techniques for mining equipment. The methodology employed was a systematic literature review, utilizing electronic databases like Scopus and Web of Science and selecting relevant studies by applying inclusion and exclusion criteria. The results indicate that the most commonly used technologies are Machine Learning, IoT, Big Data, and sensors. These technologies are applied to equipment such as conveyor belts, loaders, trucks, shovels, and crushers. Their impact includes superior fault detection accuracy, improved maintenance planning, and enhanced operational safety. This article is expected to be a theoretical reference for future research and mining companies looking to adopt these technologies.