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In the contemporary era, the intersection of the Internet of Things and artificial intelligence revolutionizes how industries monitor and optimize their operations. In this work, we present a system that combines real-time monitoring provided by Internet of Things devices with predictive analytics based on artificial intelligence. This system detects anomalies in real-time and anticipates possible failures, allowing proactive interventions to maximize efficiency and minimize operating costs. Our findings reveal a significant improvement in the early detection of abnormal trends, as the system consistently identifies potential problems long before they become critical failures. Our evaluation employed data sets collected from controlled and industrial production environments, with more than 1 million records, including critical parameters such as temperature, humidity, and pressure. The results highlight a significant improvement in the early detection of abnormal trends, with a temperature detection accuracy of 98.7%, exceeding reference values and demonstrating the system’s effectiveness in preventing critical failures. The analysis also revealed previously unrecognized operational patterns, offering opportunities for industrial process optimization. This work highlights the effective integration of the Internet of Things and artificial intelligence to improve industrial monitoring, highlighting the tangible benefits of such integration, such as the adaptability and continuous learning of the system, ensuring its long-term effectiveness.
Villegas-Ch et al. (Mon,) studied this question.