Dr. Eduardo García Villena, a researcher at the Universidad Internacional Iberoamericana (International Iberoamerican University, UNIB), is participating in a study that has developed a revolutionary method that combines artificial intelligence (AI) techniques with telemetry data to optimize the diagnostics and prognostics of smart devices.
In an increasingly interconnected world, Internet of Things (IoT) devices have transformed the way we interact with technology, from smart appliances to advanced industrial systems. However, the exponential growth of these devices poses a critical challenge: how can their efficiency, reliability and proactive maintenance be guaranteed? To that end, the study proposes an innovative approach that combines telemetry data and artificial intelligence (AI) techniques, such as forward and backward chaining, to address these needs.
These techniques, traditionally employed in expert systems, allow problems to be addressed from two complementary perspectives: while forward chaining predicts problems based on known data, backward chaining identifies causes based on observed results. By integrating them with telemetry data, which collects key information on the status of the devices, the study proposes a system that not only detects anomalies in real time, but also anticipates future failures, minimizing downtime and optimizing operational efficiency. This is crucial in fields such as industrial automation, infrastructure management and healthcare.
A dual approach to predictive maintenance
The diagnostic and prognostic engine developed in this study proved capable of identifying critical problems in IoT devices, such as temperature and pressure fluctuations, in real time. Furthermore, its predictive capacity allowed it to anticipate possible failures before they occurred, which is essential for implementing preventive maintenance strategies. For example, the system generated «high temperatura» and «low pressure» alerts based on both current data and future projections, facilitating proactive decision-making to avoid operational interruptions.
This dual approach represents a significant advantage over traditional methods, which tend to focus solely on diagnosis or prognosis. By combining both capabilities, the proposed system offers comprehensive monitoring that not only detects existing problems, but also anticipates potential risks, optimizing maintenance plans and reducing the costs associated with unexpected failures.
Final implications
Looking to the future, the study proposes several lines of work, including the integration of machine learning algorithms to complement rule-based techniques, the development of self-learning systems that adapt to changing environments, and the conducting of experiments under more diverse conditions to evaluate the robustness of the method. These initiatives could not only improve the accuracy and efficiency of the system, but also extend its applicability to a wider range of devices and operational scenarios.
The combination of artificial intelligence and telemetry data opens up new possibilities for the diagnosis and prognosis of IoT devices, offering a proactive approach that promises to transform maintenance strategies in this area. Although there are still challenges to overcome, the results of this study underline the potential of these technologies to guarantee the reliability and longevity of IoT devices, marking an important step towards a more efficient and interconnected future.
If you want to know more about this study, click here.
To read more research, check out the repository of UNIB.
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