Prognostic Health Management Using IR Thermography: The Case of a Digital Twin of a NiTi Endodontic File.

Prognostic and health management technologies are increasingly important in many fields where reducing maintenance costs is critical. Non-destructive testing techniques and the Internet of Things (IoT) can help create accurate, two-sided digital models of specific monitored objects, enabling predictive analysis and avoiding risky situations. This study focuses on a particular application: monitoring an endodontic file during operation to develop a strategy to prevent breakage. To this end, the authors propose an innovative, non-invasive technique for early fault detection based on digital twins and infrared thermography measurements. They developed a digital twin of a NiTi alloy endodontic file that receives measurement data from the real world and generates the expected thermal map of the object under working conditions. By comparing this virtual image with the real one acquired by an IR camera, the authors were able to identify an anomalous trend and avoid breakage. The technique was calibrated and validated using both a professional IR camera and an innovative low-cost IR scanner previously developed by the authors. By using both devices, they could identify a critical condition at least 11 s before the file broke.

An Improvement Strategy for Indoor Air Quality Monitoring Systems.

Air quality has a huge impact on the comfort and healthiness of various environments. According to the World Health Organization, people who are exposed to chemical, biological and/or physical agents in buildings with low air quality and poor ventilation are more prone to be affected by psycho-physical discomfort, respiratory tract and central nervous system diseases. Moreover, in recent years, the time spent indoors has increased by around 90%. If we consider that respiratory diseases are mainly transmitted from human to human through close contact, airborne respiratory droplets and contaminated surfaces, and that there is a strict relationship between air pollution and the spread of the diseases, it becomes even more necessary to monitor and control these environmental conditions. This situation has inevitably led us to consider renovating buildings with the aim of improving both the well-being of the occupants (safety, ventilation, heating) and the energy efficiency, including monitoring the internal comfort using sensors and the IoT. These two objectives often require opposite approaches and strategies. This paper aims to investigate indoor monitoring systems to increase the quality of life of occupants, proposing an innovative approach consisting of the definition of new indices that consider both the concentration of the pollutants and the exposure time. Furthermore, the reliability of the proposed method was enforced using proper decision-making algorithms, which allows one to consider measurement uncertainty during decisions. Such an approach allows for greater control over the potentially harmful conditions and to find a good trade-off between well-being and the energy efficiency objectives.